Methods and apparatus for analyzing and providing feedback of training effects, primary exercise benefits, training status, balance between training intensities and an automatic feedback system and apparatus for guiding future training

ABSTRACT

A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

RELATED APPLICATIONS

The present application is a continuation of, and claims prioritybenefit to, co-pending and commonly assigned U.S. patent applicationSer. No. 16/794,279, filed Feb. 19, 2020, which itself claims priorityto U.S. Application No. 62/807,437, filed Feb. 19, 2019. The contents ofeach application is hereby incorporated by reference in its entiretyinto the present application.

FIELD OF THE INVENTION

These methods and apparatus relate to improved recognition of anaerobicsections of exercise and to providing feedback on training load ortraining effect reflecting energy systems used and trained duringexercise

BACKGROUND OF THE INVENTION

Energy for athletic training and exercising of an individual human mayoriginate from anaerobic and aerobic sources. The anaerobic energy isproduced when the production of energy from oxygen is not fast enough tomeet the demands of external work. The substrates of anaerobic energysystems include energy from Adenosine Triphosphate (ATP) in muscles,Creatine Phosphate (PCr) resources, and muscle glycogen. Lactic acid isproduced as the end product of anaerobic metabolism, but lactic acid canbe further oxidized to yield ATP or resynthesized in liver to glycogen.The substrates of aerobic energy systems include muscle glycogen andcirculating glucose and fatty acids. The key determinants of energyproduction are exercise intensity and time related.

Traditionally, aerobic and anaerobic parts of a specific exercise havebeen differentiated by heart rate training zones, for example so thatheart rate over 90% of individual maximal heart rate has beencategorized as anaerobic and under 90% as aerobic training. In moresophisticated solutions, this limit may have been defined by individualanaerobic threshold. However, these solutions lack deep understanding ofthe relationship between heart rate responses and external workperformed and of the physiological responses eliciting trainingadaptations. Physiologically, when exercise intensity is quicklyincreased resulting in a need to use anaerobic energy pathways, therecan be a mismatch between heart rate and metabolic responses, which needto be considered in order to evaluate anaerobic contributions to energyproduction and to assess training load accurately.

We present a method and system which relates to recognizing anaerobicparts of exercise from plurality of physiological and physicalparameters. These parameters may contain information on physiologicalresponses to exercise and may include heart rate, oxygen consumption,respiration rate, EPOC, TRIM IP of which all may be analyzed fromheartbeat data. Physical parameters may be external work such as speed,acceleration, power, and also theoretical work (theoretical oxygenconsumption) performed. The system can recognize and differentiateanaerobic and aerobic sections from any type of exercise performedwithout specific exercise protocol (i.e. exercise can be freelyperformed), provide feedback of characteristics of different exercisesections (intervals), and training load contributing specifically toanaerobic and aerobic performance and energy systems. A method is alsopresented for estimation of anaerobic training effect. There are nomeans for estimation of training effect in prior art in supramaximalexercises or exercise where submaximal and supramaximal phasesalternate. Only the estimation of training effect in submaximalexercises has been disclosed (applicants own patent U.S. Pat. No.7,192,401). Since supramaximal training have been found effective inhealth enhancing purposes and fitness training, it is important to beable to estimate anaerobic training effects too. In addition, in manyanaerobic type sports—such as soccer, ice-hockey, alpine skiingetc.—anaerobic training effect may be even more important than aerobic(cardiorespiratory) training effect considering development of physicalcapacities. Therefore, the invention helps athletes (exercisers) and/orpersonal trainers and coaches (all users of the invention) to assessmore accurately the effects of training, which is crucial for optimizingthe content of training from sports-specificity and individuality pointof views. In addition, the described invention enables recognition ofintervals and estimation of anaerobic training effect in real time.Accordingly, the invention helps in optimizing training dose forathletes or keep fit enthusiasts since coaches and or personal trainers(or exercisers themselves) are able to evaluate whether to continueexercise as planned or whether to increase or decrease intensity. Theuser can see in real-time the accumulated training load and the impactof training on energy production systems which are intimately related totraining adaptations. Considering the analysis of past exercise, it isalso useful for the exerciser or coach or trainer to know the number ofintervals performed during exercise. In addition, information on thenumber and intensity of intervals as well as duration of recovery phasestogether with—or separate from —anaerobic training effect assessmentsupport analysis of physiological training effects.

SUMMARY OF THE INVENTION

The invention aims is a method and an apparatus recognize of anaerobicsections of exercise and to provide feedback on training load ortraining effect reflecting energy systems used and trained duringexercise.

The characteristic features of the method according to the invention andthe features of the apparatus implementing the method are stated in theaccompanying claims. The method according to the invention determines an‘oxygen debt’ like cumulative physiological sum (usually training effectTE as EPOC value) brought on by a change in a body homeostasis and itsaerobic and anaerobic values. Particularly anaerobic value may bedetermined by a procedure, where a total EPOC (or TRIMP) is determinedas a total sum and an aerobic part calculated in a known manner, isdeducted from the total sum. There are two main lines for implementingthe invention. The first one scans high intensity phases and recognizescharacteristics of each intervals therein using buffering andcalculating probabilities to classify intervals. Another implementationuses a different approach, where a starting edge and a starting level ofheart rate are main variables to achieve multiplication factor, whichconverts the measured intensity (% VO2max) to the anaerobic intensity.That gives a value for a positive accumulation of anaerobic ‘oxygendebt’. A recovery component and scaling may be used to obtain fullyrepeating results.

Exemplary disclosures of the embodiment may detect exercise intervals,analyze anaerobic exercise periods, analyze training effects and furtherprovide feedback.

In one exemplary embodiment, a heart rate-based method and system forrecognizing anaerobic sections of exercise and to providing feedback ontraining load or training effect reflecting energy systems andproperties used and trained during an exercise may be conductedaccording to the following exemplary steps:

-   -   a) A user may start to exercise;    -   b) Heart rate and/or other physiological response of a user may        be continuously measured by plurality of physiological        parameters, and measured value or values may be recorded with        time stamp as physiological data. Physiological parameters may        include heart rate. Unreliable data points, such as ectopic        beats of heart rate may be filtered or corrected by signal        processing first, and remaining points may form accepted data        points;    -   c) Recognition of high intensity intervals based on periods of        increasing or decreasing physiological values, for example heart        rate. High intensity intervals may be detected by analyzing the        derivatives of heart rate with regard to time, i.e. the degree        of heart rate changes. This may be done by using a data buffer        for storing and analyzing information about the derivatives in        real time; The characteristics of each interval are recorded        into a buffer. These comprise at least starting oHRmax or %        VO2max-value, final peak % HRmax or % VO2max-value and a value        depicting the end of the interval as well as timestamps of these        parameters The size of the buffer may be 16 records (generally        10-30), which means 80 s duration when 5 s frequency is used in        calculation.    -   d) The probability of the period (interval) to be anaerobic        section may be calculated based on the magnitude of the        derivatives, heart rate differences, duration of the period, and        heart rate level;    -   e) The found period can thereafter be rejected not to accumulate        anaerobic units with specific rules that may be related to        -   1) the time difference to the previous anaerobic period            being too short if the heart rate level is higher than a            certain threshold, for example 70% of HRmax, when the heart            rate starts to increase,        -   2) the highest heart rate during the period being lower than            certain threshold, for example 80% of HRmax,        -   3) the duration of the period being shorter than certain            threshold, for example 15 seconds,        -   4) the highest oxygen consumption value during the period            being lower than a certain threshold, for example 73% of            VO2max, and        -   5) the heart rate difference between the start of the period            and the last local heart rate peak value at the end of the            period being lower than a predetermined threshold value. The            predetermined threshold value (=smallest allowable            difference) may be proportional to the HRpeak;    -   f) Determining the anaerobic sum for the detected periods (high        intensity intervals)    -   g) The anaerobic sum within specified exercise periods (high        intensity intervals) that are accepted may be determined based        on different factors that can be for example duration of the        interval, the peak intensity of the interval as for example %        VO2max or % HRmax, the duration of peak intensity of interval,        and the physiological recovery status immediately before the        interval as % FHRmax level of the person. The calculated        anaerobic sum may be higher when the interval is shorter, the        intensity is higher, and when the person's recovery status        before the interval is better, i.e. heart rate is lower;    -   h) The periods (intervals) can be thereafter also categorized        into different groups, for example but not limited to “clear        anaerobic”, “weak anaerobic”, “long” based on quantification of        anaerobic sum and duration of the period;    -   i) Characteristics of different exercise periods can be        presented to the user, for example average duration and        intensity of intervals.    -   j) Determining the anaerobic sum performed during steady-state        high-intensity periods (=non-interval periods with high        intensity) where the anaerobic sum may be cumulative in nature:        For example during high exercise intensities, which may be for        example 90-100% of HRmax, certain amount of anaerobic sum is        cumulatively achieved. It may be related to exercise intensity        so that the higher the intensity (i.e. the closer the person is        his/her maximal heart rate) the higher anaerobic sum is        achieved;    -   k) Defining the total anaerobic sum for the whole exercise        period where the total anaerobic sum by putting together short        high intensity intervals and non-interval periods with high        intensity;    -   l) The total amount of anaerobic sum can thereafter be        classified by comparing the sum with an anaerobic work scale.        For example, a classification may comprise an anaerobic training        effect having values between 1-5, and having a verbal        description between very easy and very hard (overreaching)        anaerobic exercise. Classification may be based on commonly        known coaching science, i.e. anaerobic work quantities in        different exercises. In addition, physical fitness level of a        person may be taken into account when evaluating the anaerobic        load of the performed exercise. In principle, a person with        higher fitness level (or activity level) needs to get higher        anaerobic sum to achieve similar training effect;    -   m) In similar fashion, the performed aerobic sum is scaled        during exercise by comparing measured aerobic sum to reference        values for aerobic work. Aerobic sum may be measured with        physiological parameters, for example using EPOC and/or TRIMP        that are calculated based on heartbeat data. Person's physical        fitness level may be taken into account when classifying the        calculated aerobic sum. Classification of aerobic sum may        comprise aerobic training effect having values typically between        1 and 5, and having a verbal description between minor and        overreaching training effect.    -   n) Further, the proportion of anaerobic effect can be        continuously calculated by comparing aerobic and anaerobic        training effect-values

Scaling of Aerobic and Anaerobic Training Effects

-   -   1=Minor training effect/very easy    -   2=Maintaining training effect/easy    -   3=Improving training effect/moderate or somewhat hard    -   4=Highly improving training effect/hard    -   5=Overreaching training effect/very hard

Following equation (1) may be used to assess the proportion of aerobicand anaerobic training effect (TEaer and TEanaer, respectively) from theoverall training effect if both values are under training effect value 5(Overreaching).

$\begin{matrix}{{proportionofanaerobiceffect} = {\frac{TE{anaer}}{{TE{anaer}} + {TE{aer}}}.}} & (1)\end{matrix}$

If calculated anaerobic sum is higher than required for anaerobicTraining effect value 5 (Overreaching), TEanaer can be replaced in theformula by the following equation:

$\begin{matrix}{{{TE{anaer}} = \frac{anaerobicSum}{{anaerobicSumatT}E5{level}}},} & (2)\end{matrix}$

in which anaerobic sum at TE5 level is the sum required to achieveTraining Effect value 5 (Overreaching). In a similar fashion, if theaerobic sum defined by EPOC and/or TRIMP is higher than required aerobicsum for Training Effect value 5, TEaer can be replaced in the formula bythe following equation:

$\begin{matrix}{{TE{aer}} = {\frac{aerobicSum}{{aerobicSumatT}E5{level}}.}} & (3)\end{matrix}$

One exemplary embodiment comprising speed/altitude or power measurementcomprises the following steps:

-   -   1. Heart rate and external work output (speed+altitude or power        output) are measured during a user performed exercise session    -   2. Modified intensity (=theoretical VO2) can be calculated using        weighted average of heart rate and external workload. External        workload can be determined using either the combination of speed        and altitude, or power output alone. The resulting value (e.g.        ml/kg/min) may be divided by person's maximal oxygen uptake to        get proportional intensity (VO2max) estimate.        -   a. It is also possible to calculate modified intensity            solely based on external workload. However, combining            information on external workload with heart rate in            formation may significantly stabilize modified intensity            value.    -   3. Proportional intensity (VO2max) estimate is calculated based        on heart beat data.    -   4. EPOC value is pre-predicted during the exercise using the %        VO2max estimate derived from modified intensity    -   5. EPOC value is pre-predicted during the exercise using the %        VO2max estimate derived from heart beat data    -   6. Calculating continuously two different Training Effect (TE)        estimates based on two different EPOC values    -   7. Selecting the higher Training Effect value to represent the        total Training effect of the exercise or presenting both TE        values simultaneously to the user    -   8. If willing to provide aerobic and anaerobic TE contribution        to a user, dividing HR based EPOC estimate by the EPOC estimate        derived from work output. Alternatively, HR based TE can be        divided by Total TE.

Any of the calculated parameters can be given during and/or afterexercise to the user, or to any external system.

In an exemplary embodiment, labels may be applied to each trainingworkout to provide additional feedback. Feedback phrase logic is basedon determined anaerobic and aerobic training effect (anTE and aerTE,respectively) in addition to other workout criteria. A workout labelgives a description of the impacts of training session, covering bothaerobic and anaerobic training.

Workout labels are based on aerobic and anaerobic feedback phrases—eachfeedback phrase has a corresponding workout label. Each workoutaccumulates both aerobic and anaerobic load if both aerTE and anTE are1.0 or greater. Determined aerobic training load is transferred to aselected aerobic label and anaerobic training load is transferred to aselected anaerobic label. Based on the cumulative training load sum forboth anaerobic and aerobic training load, and the identified workoutlabel, the respective training load units are transferred to theparticular label. Over multiple workouts, training load units canaccumulate within specific labels and identify the proportion of thetypes of training over a given period.

Workout labels are then also used to analyze the distribution oftraining load. The distribution of training load may be displayed usingthe associated labels, and can also be simplified further into coherentintensity categories that generally describe the energy systems beingused. Distribution of training load is based on training loads collectedover a month's time or extrapolated to represent approximately a month.

The method could be implemented in any device comprising a processor,memory and software stored therein and a user interface, for example, aheart rate monitor, fitness device, mobile phone, PDA device, wristopcomputer, personal computer, and the like.

BRIEF DESCRIPTION OF THE FIGURES

Advantages of embodiments of the present disclosure will be apparentfrom the following detailed description of the exemplary embodiments.The following detailed description should be considered in conjunctionwith the accompanying figures in which:

FIG. 1 represents a sprint interval exercise where external workloadbased EPOC(=modified intensity based EPOC; modified intensity can becalculated also solely based on heart rate information) accumulates to asignificantly higher level than HR based EPOC. Training session hasimproving anaerobic training effect.

FIG. 2 represents a hard steady pace exercise where external workloadbased EPOC (=modified intensity based EPOC; modified intensity can becalculated also solely based on heart rate information) accumulates onlyslightly above HR based EPOC. Exercise has no anaerobic training effect.

FIG. 3 represents an example of interval detection wherein exerciseconsists of a plurality of different intervals. In connection withdetection intervals can be classified into different classes based ontheir characteristics (intensity gradient, peak or average intensity andduration).

FIGS. 4 a-4 d represent an example of possible effects of differentinterval characteristics on accumulation of anaerobic sum: Intervalduration, interval peak intensity (% VO2max), HR difference betweeninitial level and highest intensity during the interval, and initial HRlevel (% HRmax).

FIG. 5 represents an example of scaling of anaerobic sum into a trainingeffect value wherein activity class has an effect on the scaling. Forthe person with higher physical fitness, higher anaerobic sum is neededto achieve similar training effect than for a person with poorerphysical fitness level or lower activity level.

FIG. 6 represents an example of user interface with a summary viewwherein the contribution of aerobic and anaerobic training effects, andthe total training effect of several athletes is shown on a singledisplay.

FIG. 7 represents an example of user interface with a real time groupview wherein the contribution of aerobic and anaerobic training effects,the total training effect, and current exertion level of severalathletes is shown on a single display.

FIG. 8 represents an example of HR-only based calculation.

FIG. 9 represents an example of calculation in situation where both HRand external workload information are available.

FIG. 10 represents an example of user interface with a display thatallows comparison of different days as team level averages as well asplayer rankings within different days.

FIG. 11 represents an example of a hardware assembly.

FIG. 12 represents an example of embodiment where speed, altitude and HRinformation are utilized in determining modified intensity. As can beseen, modified intensity can reach much higher values than one whatmight expect based on HR-level information. As is known from literatureMET values correspond to km/h values very well in running. As can beseen from the figure modified intensity matches well with measuredspeed. Surprisingly, modified intensity corresponds well with speed(km/h) even when it is calculated solely based on heart rate.

FIG. 13 represents an example of the accumulation of EPOC as a functionof modified intensity.

FIG. 14 represents an example of calculation of modified intensity withrespect to how it may increase and decrease.

FIG. 15 presents “Table 1 Short feedback phrases”

FIG. 16 presents “Table 2 Long feedback phrases”

FIG. 17 presents an example table of an alternative embodiment for theselection of anaerobic feedback

FIG. 18 presents an example table of an alternative embodiment for theselection of aerobic feedback

FIG. 19 represents an example selection logic for determining theaerobic and anaerobic workout label for each activity

FIG. 20 represents example anaerobic feedback phrase, both “short” and“long” phrases.

FIGS. 21 a and 21 b represent example aerobic feedback phrases.

FIG. 22 displays an example user interface showing training loaddistribution

FIG. 23 displays an example user watch-style device user interfaceshowing workout result label feedback.

FIG. 24 displays an example watch-style device user interface showingmultiple screens of training load balance feedback.

FIG. 25 shows a table with sample monthly training load limits based onactivity class.

The Figures may show exemplary embodiments of the system, method,computer product, and apparatus for detecting exercise intervals,analyzing anaerobic exercise periods, and analyzing individual trainingeffects as herein described. Figures are only exemplary, and they cannotbe regarded as limiting the scope of invention.

DETAILED DESCRIPTION

The following table shows some exemplary definitions and abbreviationsof terms used in the exemplary embodiments described herein.

Term or abbreviation Definition Anaerobic Training Effect Thephysiological impact or effect the training has on anaerobic performancecalculated by analyzing the anaerobic sum (e.g. analyzing high-intensityintervals) and by scaling that based on commonly known quantities ofanaerobic work in different exercises. Anaerobic sum The physiologicalstimulus caused by the anaerobic work performed during exercise.Anaerobic sum is calculated by detecting anaerobic sections andcontinuous high-intensity work from exercise High intensity interval Aperiod of continuous work during an exercise having typically higherintensity than during rest or recovery periods within exercise. Anintermittent (interval-type) exercise typically includes two or moreintervals. Clear anaerobic interval An anaerobic high intensity intervalwith a higher anaerobic sum than a certain threshold value Weakanaerobic interval An anaerobic interval with a lower anaerobic sum thana threshold value that is required for a clear anaerobic interval Longinterval An interval that is longer than a certain threshold value, e.g.200 seconds Aerobic Training Effect The physiological impact or effectthe training has on aerobic performance calculated by analyzing theaerobic sum performed (e.g. using EPOC, TRIMP) and scaling the sum basedon commonly known quantities of aerobic work in different exercisesAerobic sum The physiological stimulus caused by aerobic work performedduring exercise and calculated by assessing EPOC and/or TRIMP duringexercise Anaerobic threshold = AnT Anaerobic threshold refers to thehighest velocity or external power output that a person's can maintainduring physical activity without continuous lactic acid accumulation.AnT can be determined automatically during a user performed highintensity exercise where heart beat interval data and external workloadare measured. HR Heart rate (beats/min) HRmax maximum heart rate (of aperson) (beats/min) ΔHR Change of heart rate level % HRmax heart raterelative to maximum heart rate VO2 Oxygen consumption (ml/kg/min) VO2maxmaximum oxygen consumption capacity of a person (ml/kg/min) thatreflects cardiorespiratory fitness level of the person % VO2max measuredor estimated VO2 relative to VO2max of a person - may be calculatedusing either HR level information or HRV information RespR Respirationrate that can be derived e.g. based on heart rate variability measuresTheoretical VO2 or theoretical Value that describes external workload(ml/kg/min). oxygen consumption Can be calculated based on speed andaltitude change (or speed and grade of inclination), or based onmeasured power output in bicycles and other exercise equipment ormeasured power output during running. on/off-kinetics informationInformation related to heart beat data that reflects the rate of changein HR based VO2 estimate Δt Refers to instant time or change in time ORduration Activity level/activity class (AC) Refers to person's physicalactivity level and how much the person is used to exercise. For example,has an effect for following: How much exercise can be tolerated by theperson without overstraining one's body OR what is the quantity ofexercise that is needed achieve a given training effect.METmax/maxMET/maximal_MET maximum oxygen uptake capacity of a personrelative to resting oxygen consumption = VO2max (ml/kg/min)/resting VO2(ml/kg/min) = VO2max (ml/kg/min)/3.5 ml/kg/min Modified intensityIntensity estimate that depicts true oxygen requirement during exerciseas ml/kg/min or METs or with respect to VO2max (% VO2max). Modifiedintensity can be calculated using a combination of external workload andheart rate or using only either one of them alone. R-R-interval = RRITime interval between successive heart beats in ECG- signal that ismeasured using e.g. a heart rate monitor. Analysis of R-R intervals (=heart rate variability) allows assessment of e.g. respiration rate inaddition to heart rate. Measurement of RRI is not mandatory for applyingthe methods described in this document. Beat-to-beat signal derived frome.g. PPG-signal can be used as well. In addition, all methods can beapplied also using heart rate level information from either ECG or PPGsignal. HRV Heart rate variability meaning the variation in timeinterval between successive heart beats. The magnitude of heart ratevariability may be calculated from electrocardiogra orphotoplethysmographic signals, for example. Freely performed physicalexercise An exercise that may be performed without a specific protocol.The user may freely decide the intensity of exercise, as well asrecovery periods inside the exercise session. Continuous measurementHeart beats may be recorded beat-by-beat or 1-15 sec intervals andexternal power with similar 0.1-15 sec intervals. Calculation of resultsmay be performed with 1-15 sec intervals where 1-5 sec frequency mayenable better accuracy. (Selected number of values are recorded)Training load A measure of accumulated load caused by training. Thehigher the training load the higher is also training stimulus. EPOC andTRIMP are typically used measures of training load. TRIMP (Trainingimpulse) A cumulative measure describing training load. TRIMP is merelya mathematical index, not a physiological measure EPOC (Excesspost-exercise EPOC reflects the extent of disturbance in body's oxygenconsumption) homeostasis brought on by exercise. As it can be nowadaysestimated or predicted - based on heart rate or other intensityderivable parameter - it can be used as a cumulative measure of trainingload in athletic training and physical activity. Non-interval periodTime during exercise that is either recovery phase (or low intensityphase) between high intensity intervals OR high intensity exercisingperiod that cannot be regarded as interval training. Workout labelsWorkout labels give a simple description of the impacts of trainingsession and it covers both aerobic and anaerobic training. Workoutlabels are based on aerobic and anaerobic feedback phrases - eachfeedback phrase has a corresponding workout label (see FIG. 19). Eachworkout may accumulate both aerobic and anaerobic load if both aerobicTE and anaerobic TE are 1.0 or greater. Aerobic load is transferred toselected aerobic label and anaerobic load is transferred to selectedanaerobic label. E.g. if workout's aerobic load // label are = 75 // #2and anaerobic load // label = 25 // #7; then 75 units of aerobic load intransferred to aerobic base-label and 25 units of load to speed-label.Training load distribution Workout labels are used to analyze trainingload distribution. While “time in zone analysis” over week(s) has beenpossible already, labels provide benefits over time in zone approach asthey help to differentiate between well-structured training from poorlyplanned training. For example, traditional “time in zone” analysis mayhave a good distribution of intensities in long term analysis even if aperson performs high intensity interval training each time he/sheexercises since time at low aerobic intensities accumulate duringwarm-ups and cool downs. No major changes would be recommended to futuretraining. However, applicant's present invention would reveal the excessamount of high intensity training and that the person should performmore low intensity workouts in the future to maintain balance in thedevelopment of body's energy systems. Additionally, traditional heartrate based intensity zone model does not and cannot provide anyinformation on accumulated time or effort at supramaximal intensitiesthus excluding different kind of anaerobic training (speed endurance andpure speed) from the overall training load distribution.

Aspects of the invention are disclosed in the following description andrelated drawings directed to specific embodiments of the invention.Alternate embodiments may be devised without departing from the spiritor the scope of the invention. Additionally, well-known elements ofexemplary embodiments of the invention will not be described in detailor will be omitted so as not to obscure the relevant details of theinvention. Further, to facilitate an understanding of the descriptiondiscussion of several terms used herein follows.

As used herein, the word “exemplary” means “serving as an example,instance or illustration.” The embodiments described herein are notlimiting, but rather are exemplary only. It should be understood thatthe described embodiments are not necessarily to be construed aspreferred or advantageous over other embodiments. Moreover, the terms“embodiments of the invention”, “embodiments” or “invention” do notrequire that all embodiments of the invention include the discussedfeature, advantage or mode of operation.

Further, many of the embodiments described herein are described in termsof sequences of actions to be performed by, for example, elements of acomputing device. It should be recognized by those skilled in the artthat the various sequences of actions described herein can be performedby specific circuits (e.g. application specific integrated circuits(ASICs)) and/or by program instructions executed by at least oneprocessor. Additionally, the sequence of actions described herein can beembodied entirely within any form of computer-readable storage mediumsuch that execution of the sequence of actions enables at least oneprocessor to perform the functionality described herein. Furthermore,the sequence of actions described herein can be embodied in acombination of hardware and software. Thus, the various aspects of thepresent invention may be embodied in a number of different forms, all ofwhich have been contemplated to be within the scope of the claimedsubject matter. In addition, for each of the embodiments describedherein, the corresponding form of any such embodiment may be describedherein as, for example, “a computer configured to” perform the describedaction.

The method can be implemented in versatile devices, which have resourcesfor measuring physiological responses (e.g. Oxygen consumption, heartrate, etc.) and external workload (e.g. speed and altitude or poweroutput), and run software to execute processes depicted in the exemplaryflowcharts of FIGS. 8 and 9 . Model considering HR-only basedcalculation is disclosed in FIG. 8 . The calculation has the followingsteps:

-   -   User starts measurement (10)    -   Beat to beat HR data (for example RR-intervals=RRI) or HR level        data is collected (12)    -   Artefacts may be detected and corrected (14)    -   Searching high intensity intervals from corrected HR signal and        filtering out high intensity intervals that are not anaerobic        (16)    -   Calculating accumulated aerobic sum (e.g. EPOC/TRIMP) as well as        anaerobic sum (e.g. EPOC/TRIMP) and determining aerobic training        effect and anaerobic training effect based on the sum values        (18)    -   Interval-count and time in different high intensity training        zones can be shown to a user (20)

Calculation comprising both HR and external workload is disclosed inFIG. 9 . The calculation has the following steps:

-   -   User may start a measurement (30)    -   Beat to beat HR data (for example RR-intervals=RRI) or HR level        data is collected. In addition, external workload is measured        for example as speed & altitude or power output time series (32)    -   Artefacts may be detected and corrected (34) from HR data    -   Calculating modified intensity from corrected HR signal and        external workload.    -   Calculating accumulated aerobic sum (e.g. EPOC/TRIMP) as well as        anaerobic sum (e.g. EPOC/TRIMP) and determining aerobic training        effect and anaerobic training effect based on the sum values        (38)    -   Interval-count and time in different high intensity training        zones can be shown to a user (40)

A schematic hardware assembly is depicted in exemplary FIG. 11 .

The system and method according to the exemplary embodiments can beapplied in many kinds of devices as would be understood by a person ofordinary skill in the art. For example, a wrist top device with aheart-rate transmitter, a mobile device such as a phone, tablet or thelike, or other system having CPU, memory and software therein may beused.

According to exemplary FIG. 11 , in the implementation may include anassembly built around a central processing unit (CPU) 62. A bus 66 maytransmit data between the central unit 62 and the other units. The inputunit 61, ROM memory 61.1, RAM memory 61.2 including a buffer 63, keypad78, PC connection 67, and output unit 64 may be connected to the bus.

The system may include a data logger which can be connected to cloudservice, or other storage as would be understood by a person of ordinaryskill in the art. The data logger may measure, for example,physiological response and/or external workload.

A heart rate sensor 72 and any sensor 70 registering external workloadmay be connected to the input unit 61, which may handle the sensor'sdata traffic to the bus 66. In some exemplary embodiments, the PC may beconnected to a PC connection 67. The output device, for example adisplay 75 or the like, may be connected to output unit 64. In someembodiments, voice feedback may be created with the aid of, for example,a voice synthesizer and a loudspeaker 75, instead of, or in addition tothe feedback on the display. The sensor 70 which may measure externalworkload may include any number of sensors, which may be used togetherto define the external work done by the user.

More specifically the apparatus presented in FIG. 11 may have thefollowing parts for determining an anaerobic training effect:

-   -   a heart rate sensor 72 configured to measure the heart beat of        the person, the heart rate signal being representative of the        heart beat of the user;    -   optionally at least one sensor 70 to measure an external        workload during an exercise, and    -   a data processing unit 62 operably coupled to the said sensors        72, 70, a memory 61.1, 61.2 operably coupled to the data        processing unit 62,    -   the memory may be configured to save background information of a        user, for example, background data including an earlier        performance level, user characteristics, and the like.

The apparatus may include dedicated software configured to execute theembodiments described in the present disclosure.

In one exemplary embodiment initial background and personal data may bestored. For example, the performance level (for example VO2max orMETmax) and the maximum heart rate (HRmax), and the like, of the usermay be stored. Personal data may be entered or determined beforehand.

In one exemplary embodiment, a person (e.g. an athlete or keep fitenthusiast) may start an exercise session. The type of exercise can beeither interval or continuous, i.e. it can include breaks and restperiods. The user can freely decide the intensity of exercise, as wellas recovery periods inside the exercise session. Heart beat data andperformance data can be continuously measured (speed and altitude orpower output) during the exercise using, for example, a heart ratemonitor, wristop computer or other related device as would be understoodby a person of ordinary skill in the art. Even a heart beat sensor thatis connected to a mobile phone or PDA device (using for exampleBluetooth connection) can be used, in which case the mobile phone or PDAdevice would measure external workload (speed and altitude) and serve asa CPU unit.

In further exemplary embodiments the user may exercise outdoors. Bothheart rate (or other physiological signal) and external workload can bemeasured to achieve the most accurate analysis of anaerobic trainingeffects. The user can exercise, for example, by walking, running, orplaying sports such as football, rugby, field hockey, tennis, or anyother sports. In some embodiments heart rate may be measured using aheart rate transmitter belt, or the like, and analyzed in a CPU-unitthat can be, for example, a normal sports watch, wristop computer, orsimilar device as would be understood by a person of ordinary skill inthe art. Alternatively, it may be possible to use ppg(photoplethysmograph)-signal processing so that both the measurement andanalysis of data may be done using a wristop device, or the like.Measurement of speed and altitude can be done using a GPS signal. TheGPS receiver may be embedded, for example, in the wristop device, but anexternal GPS receiver can be used as would be understood by a person ofordinary skill in the art. Altitude data can be retrieved from GPS data,additional barometer data, and the like. A barometer may be embedded inthe wristop computer. In the described exemplary embodiments, a usermay, for example, walk or run (or both) during the exercise. The terraincan be whatever the user wants, for example, hilly or flat. During theexercise, data points may be continuously filtered and/or validated. TheTraining Effect or any parameter calculated by the system can be shownto the user during the exercise, or after exercise, as desired.

In some of the above described exemplary embodiments, heartbeat data,speed data and altitude data may be gathered and used, for example, whenthe user is exercising on foot (walking/pole walking or running)outdoors. In still further exemplary embodiments, a WIFI technique, forexample, may be used so that positioning can be performed indoors. Itmay also be possible to use an accelerometer signal (for example anaccelerometer positioned on a user's foot or the like) to definewalking/running speed indoors or outdoors, and that data can be usedtogether with barometer data. It is also possible that the exercise isdone using a treadmill, or the like. In that case, it is also possiblethat the speed data can be retrieved from an accelerometry signal, orthe like. In one exemplary embodiment a user can input treadmill speeddata to the CPU while the heartbeat data is continuously measured.

Furthermore, considering the embodiments that use both physiological andexternal workload data, it is also possible to determine the anaerobictraining effect (or other such parameters) in other exercise modes: Forexample in cycling or rowing power output can be easily measured andretrieved. As would be understood by a person of ordinary skill in theart, power output can be measured in cycling, for example, using a powermeter embedded in pedals or chains, and this power data can be shown tothe user in a wristop device, or the like. In one exemplary embodimentrelated to cycling—speed and altitude data may be replaced with poweroutput data measured from a bicycle. The user can do the bicyclingexercise indoors or outdoors, and on any desired terrain.

Referring still generally to the exemplary embodiments, wherephysiological and external workload data are measured, (e.g. cycling orspeed and altitude of walking or running are measured) it is possible toincrease the accuracy of Training Effect estimate by measuring externalworkload data. This is because heartbeat data can be measuredcontinuously as a function of performance data.

Since purely HR and/or HRV based assessment of anaerobic/aerobictraining effect may be beneficial in some cases, these exemplaryembodiments are presented below. Purely HR and/or HRV based assessmentmay be more desired for example in ice-hockey, skating or other sportswhere external work output is difficult to measure. In addition,positioning indoors is more difficult than outdoors that may leadathletes and coaches to select HR and/or HRV based assessment for indoorexercises.

In one exemplary embodiment disclosing a purely HR and/or HRV basedassessment, the system constantly detects exercise intervals fromperiods of increasing and decreasing heart rate from the heart beatdata. This is done by the system by calculating a moving average of 10second heart rate difference. The average is calculated for eachmeasurement point by weighting the differences (calculated for thesurrounding points) by, for example, a 25 second Hanning window. Theaveraged heart rate differences are used to define the periods ofincreasing and decreasing heart rate that follow each other in the data.Of each detected period of increasing or decreasing heart rate, certainparameters are saved in the buffer memory. These are 1) the sum of theaveraged heart rate differences during the period of increasing heartrates, 2) the sum of the averaged heart rate differences during theperiod of decreasing heart rates (the sums are hereinafter denoted by pfor heart rate increases and n for decreases), 3) the initial heart rateof the heart rate increase, 4) the initial heart rate of the heart ratedecrease (HRlow for a heart rate increase and HRpeak for a decrease), 5)the time point where HRlow or HRpeak was measured, as well as 6) thepeak intensity as % VO2max at the point of the HRpeak value. Theaforementioned values are stored in timely order to a constant size databuffer. From the buffer, the oldest values are removed as new periods ofincreasing and decreasing heart rate are detected or as intervals aredetected and/or accepted.

In another exemplary embodiment, the information stored in the databuffer is used to detect exercise intervals when some of the followingapply: 1) a maximum amount of increasing and decreasing heart rateperiods has been stored in the buffer, or 2) the heart rate level hasdecreased at least 10 bpm (beats per minute) and the duration of theheart rate decrease has been at least 30 seconds, or 3) the heart ratelevel drops below 70% of the personal maximum HR. The heart rate datastored in the buffer is used to detect intervals by calculating a valuel that represents the interval-likeness of a measured heart rate timesequence. These sequences start from a heart rate increase (buffer indexi) and end to a heart rate decrease (index f). Such sequences aredefined for all values of i and f (i≤f) that are stored in the buffer.The interval-likeness is calculated for each of these sequences. Theaffecting factors include heart rate derivatives, heart ratedifferences, and the duration of the sequence. The following formula canbe used:

l=(HR _(peak,f) −HR _(low,i))+p _(i) +n _(f)+min(p _(i) ,n_(f))−l/50−Y,  (4)

where HR_(peak,f) is the last local heart rate peak value inside thesequence, and HR_(low,i) is the initial heart rate of the sequence. Theduration of the interval 1 is the duration between HR_(low,i) andHR_(peak,f) in seconds. The sum p_(i) corresponds to the first heartrate increase in the sequence and n_(f) corresponds to the last heartrate decrease, and min(p_(i), n_(f)) is the smaller of these two. Theterm Y describes the effect of the heart rate changes within thesequence and is calculated as

Y=√{square root over (Σ_(j=i+1) ^(f) p _(j) ²+Σ_(k=i) ^(f−1) n _(k)²)}.  (5)

In one exemplary embodiment, intervals are accepted for later analysis.For the sequence to be accepted as an interval, certain rules must befulfilled. These may be 1) the value I must be higher than a thresholdvalue, and it must be greater than those of other time sequences thatinclude some of the same heart rate increases and decreases than thesequence of interest (i.e. the time sequences are partly or totallyoverlapping), 2) the recovery time from the preceding interval must belonger than 30 seconds or HR_(low,i) must be lower than 70% of thepersonal maximal heart rate, 3) the highest heart rate of the sequencemust be higher than 80% of the personal maximal heart rate, 4) theduration of the sequence must be longer than 15 seconds, 5) the peakvalue for % VO2max must be higher than 73%, and 6) thedifferenceHR_(peak,f)—HR_(low,i) must be sufficiently high (the requireddifference is the larger the lower the value of HR_(low,i) is). If thesequence is accepted as an interval, the heart rate information of thesequence and of those preceding it is removed from the data buffer.

In other exemplary embodiment, detected intervals can be used tocalculate accumulated anaerobic sum of exercise. The total anaerobic sumof exercise may be the sum of 1) the anaerobic sum calculated forintervals and 2) the anaerobic sum of long continuous high intensityexercising. One significant determinant of anaerobic sum may be theduration of the interval. Duration is further multiplied by four factorswhich describe the properties of the interval (see FIG. 4 ). Thesefactors are 1) duration of the interval, 2) peak intensity of theinterval as % VO2max, 3) the starting heart rate level of the intervalthat describes the recovery level, and 4) the difference between themoving HR average at the start of the interval and the HRpeak valueinside the interval. The duration affects in principle so that theshorter the duration the higher the multiplier. If the duration of theinterval is longer than a maximal threshold value, for example 300seconds, the effective duration value used in the anaerobic sumcalculation can be fixed to the threshold value, so that the anaerobicsum of longer intervals will not be zero. The second multiplier is thehigher the higher the peak intensity (% VO2max) of the interval (notethat the interval is rejected if the peak intensity is not high enough).The third multiplier is the higher the lower the heart rate is when theinterval starts, i.e. the better the recovery level at the onset of theinterval. The fourth multiplier is directly proportional to thedifference between the HRpeak value and a moving average calculated fromthe HR values before the start of the interval.

In another exemplary embodiment, in addition to the aforementionedmultipliers, also the fluctuations in heart rate within the intervaldescribing noticeable changes in working intensity can be taken intoaccount. The fluctuation can affect the calculated anaerobic sum whenthe intensity is high enough, for example at least 80% of the personalmaximum heart rate. More anaerobic sum can be calculated when there aresignificant and regular fluctuations in heart rate within an interval.This fluctuation based anaerobic sum can be calculated by the formula

Σ_(i)α·min(n _(i) ,p _(i))  (6)

where n_(f) and p_(i) correspond to the decrease in heart rate beforethe local minimum heart rate (indexi) and the increase in heart rateafter the local minimum heart rate, respectively. The sum is calculatedover the local minimums within the interval.

The factor α can be affected by the factors described in the previousexemplary embodiment.

In another exemplary embodiment, anaerobic sum may be calculatedcumulatively from the measured HR data at each point of the exerciseeven in the case of steady state exercise (=non-interval periods). Theamount of the cumulative anaerobic sum can be affected for example bythe temporal value of HR signal, time derivative of HR signal, locallowest and peak values of IR signal, average of the HR signal, andpersonal background parameters (for example anaerobic threshold heartrate, VO2max etc.). For example, when the intensity is above 90% HRmax,the rate of increase of the cumulative anaerobic sum may be directlyproportional to the intensity, so that at 100% HRmax intensity thecumulative anaerobic sum can increase for example 0.06 units/s. This isin line with physiology since there is always anaerobic metabolism,especially above the anaerobic threshold heart rates or intensities.

In other exemplary embodiment, anaerobic sum can be used in determiningthe anaerobic training effect with linear functions. The properties(derivative and zero) of the functions are affected by the user'sactivity level or fitness level. Examples of such functions can be foundin FIG. 5 . For the person with higher physical fitness, higheranaerobic sum is needed to achieve similar training effect than for aperson with poorer physical fitness level or lower activity level.

In one exemplary embodiment, each user's individual anaerobic thresholdmay be inputted to the system. This may be performed manually, fromsoftware or by recognized automatically from exercise parameters (heartrate beat interval data and external workload data required). Individualanaerobic threshold can be used to modify the calculations in order torecognize and take into account more individually the anaerobic workperformed. For example, the effect of exercise intensity duringintervals can increase the calculated anaerobic sum if the user'sanaerobic threshold is lower than default value 90%. In similar fashion,if a person's anaerobic threshold is higher than default 90%, e.g. 93%,less anaerobic sum may be calculated.

In one exemplary embodiment, the time between the detected and/oraccepted intervals calculated by the system can be defined to representrecovery time between the intervals.

In one exemplary embodiment, after the intervals have been detected,information regarding the intervals can be provided for the user inreal-time or any time after the exercise. These information may includefor example number of intervals, intervals distribution to differentcategories (such as clear anaerobic, weak anaerobic, long interval), theintensity (e.g. average, peak, and lower level of intensity) duringintervals, duration of intervals, duration of recovery phases,parameters defining recovery phases (e.g. average, peak, and lower levelof intensity), the overall anaerobic sum calculated, the anaerobic sumcalculated within intervals, the anaerobic sum calculated outsideintervals (i.e. by continuous high-intensity exercising). Theinformation and feedback, of the examples above, can be provided to theuser in visual, numerical and verbal form, and this may include all orsome of the aforementioned parameters but not limited to these.

In one exemplary embodiment, information on aerobic and anaerobictraining effect may be provided to the user. The training effect caninclude the overall training effect (the highest of aerobic andanaerobic training effect), both or one of the training effects (aerobicand anaerobic), and the distribution of training effect into aerobic andanaerobic.

A practical example of anaerobic sum calculation based on anaerobicinterval detection

-   -   Anaerobic interval detection        -   During one time period of a high intensity interval            training, heart rate (HR) behaves as follows.            -   From 90 bpm to 170 bpm in 1 minute; from 170 bpm to 140                bpm in 30 seconds; from 140 bpm to 165 bpm in 30                seconds; from 165 bpm to 100 bpm in 1 minute.            -   Hereby the duration l of the period is 180 seconds, and                the values of the positive and negative changes in HR,                p_(i) and n_(i), are

p ₁=80,n ₁=30,p ₂=25,n ₂=65.

-   -   The interval likeness of the period can now be calculated        according to the equations (4) and (5) as

I=(165−90)+80+65+min(80,65)−180/50−√{square root over (25²+30²)}≈242

-   -   -   The period is now determined to have the following            properties.            -   The interval likeness of the period is higher than an                empirically determined threshold value.            -   The interval likeness of the period is higher than any                other periods comprising of some of the HR changes                inside the period.            -   The recovery time preceding the period (time between the                previous potential anaerobic interval and the period) is                longer than 30 seconds.            -   The maximum HR value is above 80% HRmax.            -   The length of the period is between 15 and 200 seconds.            -   The difference between the maximum HR value and the                initial HR value is higher than 20 bpm.        -   Based on these properties, the period is now validated as a            proper anaerobic interval.

    -   Calculation of the anaerobic sum

The anaerobic sum (the “anaerobic effect”) of the interval is nowcalculated by multiplying the duration of the interval, 180 seconds, bythe coefficients shown in FIGS. 4 a-4 d . The affecting coefficients arerelated to

-   -   interval duration (coefficient value=0.25)    -   Peak % VO2max (coefficient value=1.1)    -   The difference between peak % HRmax and initial % HRmax moving        average, and (coefficient value=1.0)    -   Initial % HRmax (coefficient value=1.0)

After applying the coefficients to the duration of the interval, theresulting anaerobic sum is 49.5.

Additional anaerobic sum based on the HR fluctuations is calculated bythe equation (6) to be

2.5·min(30,25)=62.5

Hereby the total anaerobic sum of the anaerobic interval is49.5+62.5=112.

The minimum buffered information needed here comprises rise and fallinformation; % HRmax-differences, timestamps, peak values of % VO2max(or % HRmax).

In following exemplary embodiments, information on performed externalwork (e.g. pedaling power in cycling OR speed/altitude changes inrunning) can be used to compare theoretical oxygen consumption to heartbeat based oxygen consumption to assess energy provided by anaerobicenergy pathways, and to assess training effect achieved using both ofthe energy pathways. This information can support or substitute theHR/HRV based calculation of anaerobic sum. Of course, use of purelyheart rate-based estimation of anaerobic and aerobic training effectenables application of the method in all sports. Use of speed andaltitude (e.g. running) or power output (cycling, rowing or otherexercise equipment) allows even a more detailed analysis of anaerobictraining effect. In addition, measurement of power during running hasrecently become possible. Running power can be measured using eitherspeed and altitude OR speed/altitude in combination with acceleration.

One exemplary embodiment comprising speed/altitude or power measurementcomprises the following steps:

-   -   1. Heart rate and external work output (speed+altitude or power        output) are measured during a user performed exercise session    -   2. Modified intensity (=theoretical VO2) can be calculated using        weighted average of heart rate and external workload. External        workload can be determined using either the combination of speed        and altitude, or power output alone. The resulting value (e.g.        ml/kg/min) may be divided by person's maximal oxygen uptake to        get proportional intensity (% VO2max) estimate.        -   a. It is also possible to calculate modified intensity            solely based on external workload. However, combining            information on external workload with heart rate in            formation may significantly stabilize modified intensity            value.    -   3. Proportional intensity (% VO2max) estimate is calculated        based on heart beat data.    -   4. EPOC value is pre-predicted during the exercise using the %        VO2max estimate derived from modified intensity    -   5. EPOC value is pre-predicted during the exercise using the %        VO2max estimate derived from heart beat data    -   6. Calculating continuously two different Training Effect (TE)        estimates based on two different EPOC values    -   7. Selecting the higher Training Effect value to represent the        total Training effect of the exercise or presenting both TE        values simultaneously to the user    -   8. If willing to provide aerobic and anaerobic TE contribution        to a user, dividing HR based EPOC estimate by the EPOC estimate        derived from work output. Alternatively, HR based TE can be        divided by Total TE.

As can be seen from the FIG. 1 , theoretical VO2 based EPOC values gethigher values than HR based EPOC values during an interval exercise. Thefigure presents and interval workout having intervals of 3×15 km/h+3×20km/h+3×23 km/h wherein each interval is followed by an active recoveryperiod including running with 10 km/h speed. Duration of recoveryrunning periods is equal to high intensity running periods. The runnerin this example has VO2max of 70 ml/kg/min that corresponds to runningspeed of 20 km/h on running track. As can be seen from the figure, whenrunning 20 km/h or slower accumulation of HR based EPOC is only slightlyslower when compared with EPOC derived based on Theoretical VO2. This isbecause HR based VO2 estimate (that reflects the actual VO2 of therunner) reaches theoretical VO2 (that describes the actual need foroxygen for any given speed) during the intervals that are run belowVO2max intensity (=20 km/h=70 ml/kg/min). With higher intensitiestheoretical VO2 is significantly higher than HR based VO2, which causesthe increasing gap between the two EPOC estimates. From a physiologicalpoint of view the EPOC value derived based on theoretical VO2 is muchmore accurate in describing total EPOC since it well reflects theincreasing gap between body's oxygen requirement and oxygen supply. Thedifference in body's oxygen requirement and oxygen supply results inincreasing oxygen deficit that is “paid” after exercise as oxygen debt.Actually, EPOC is only partly caused by oxygen debt as there are manyother components affecting: for example, exercise induced elevated bodytemperature, respiratory activity, increased level of catecholamine'setc. Calculation method for EPOC is the same but the difference islargely due to the fact that Theoretical VO2 in this example reachesvalues up to 114% VO2max whereas HR-based VO2 can only reach values upto 100%. The two calculated EPOC peak values are 74 ml/kg and 94 ml/kgfor HR based and theoretical VO2 based EPOC, respectively. Accordingly,the runner may be shown that total training effect was 3.5 and theeffect was 79% aerobic (=74/94) and 21% (=20/94) anaerobic. An oppositeexample is presented in FIG. 2 where same person has performed a hardsubmaximal steady pace exercise where heart rate based EPOC is 91 ml/kgand theoretical VO2 based EPOC 94 ml/kg. Accordingly, total trainingeffect is 3.5 but aerobic contribution is 97% and anaerobic contributionis only 3%.

Although prior art discloses comparison of used energy systems duringexercise it does not provide means to estimate the actual trainingeffect. Actually, comparison of proportions of aerobic and anaerobicenergy yield is usually not meaningful since in long exercises most ofenergy is produced aerobically even if exercise would include hardanaerobic periods. On the contrary, EPOC provides a more sophisticatedmeasure for training effect as it is a well-established measure oftraining effect. EPOC actually reflects the extent of disturbance inbody's homeostasis caused by exercise. EPOC can be modelled, forexample, using neural network modelling with a large amount ofexperimental data.

In such a case, total training effect is calculated using HR based EPOC(that is higher) and the aerobic effect would be 100% and anaerobiceffect 0%. This makes sense also from a physiological point of viewsince actual measured VO2 has a slow component meaning that in prolongedexercises VO2 drifts to a higher level than theoretical VO2.

For example, the following calculation formulas can be used fortheoretical VO2:

TheoreticalVO2 of running(ml/kg/min)=0.2*(speed m/min)+0.9*(speedm/min)*TAN(grade of incline)+3.5

TheoreticalVO2 of walking(ml/kg/min)=1.78*(speed m/s)*60*(TAN(grade ofincline)+0,073)

A threshold speed of e.g. 7.5 km/h can be used in switching from walkingformula to running formula. Alternatively, detection between walking andrunning can be used using accelerometer data.

In cycling, power output can be converted to VO2 using the followingexemplary formula:

TheoreticalVO2 of cycling(ml/kg/min)=((power watts)*12+300))/person'sweight

TheoreticalVO2(Indoor)rowingVO2(ml/kg/min)=(14.72*Power+250.39)/person'sweight

In addition, equations have been described for the calculation of roadcycling power based on measured speed and altitude data etc. based onwhich % VO2max can be calculated.

In one exemplary embodiment the accuracy of theoretical VO2 calculationis improved in interval type sports. As is known in the art, theoreticalVO2 of accelerated or decelerated running at any given speed differsignificantly from steady-speed running. For example, duringacceleration phase a runner may have an average speed of 15 km/h duringa sampling period. In this case, for example, if initial speed has been0 km/h and end speed 30 km/h, the average value of 15 km/h provides toolow estimate of theoretical VO2. Accordingly, using acceleration as amultiplying factor the error can be avoided.

In one exemplary embodiment HR-only based calculation of anaerobictraining effect can also be applied without interval detection. In thatcase calculation would go as follows:

-   -   a) A user starts to exercise;    -   b) Heart rate (beat-by-beat heart rate or HR-level) of a user is        continuously measured and recorded with time stamp,    -   c) Unreliable data points, such as ectopic beats of heart rate        may be filtered or corrected by signal processing first, and        remaining points may form accepted data points;    -   d) Determining user's modified intensity (% VO2max) from data        utilizing information on e.g. HR level relative to his/her        maximal heart rate (% HRmax), RRI derived respiration (if R-R        intervals are available) and heart rate derivative (e.g. %        HRmax) and VO2 derivative (e.g. % VO2max)        -   The calculated modified intensity gets higher when 1) HR            level goes closer to HRmax, 2) respiration rate increases 3)            when HR increases rapidly    -   e) Calculating aerobic EPOC using ordinary HR derived intensity    -   f) Calculating anaerobic EPOC using modified intensity        -   Modified intensities lower than a predetermined limit (e.g.            80% VO2max) may be excluded from calculation if only the            anaerobic contribution of high intensity work periods is            regarded meaningful. (there is always overlap in energy            production meaning that even low intensity exercise has            little anaerobic contribution. Anaerobic contribution of            energy production increases significantly above anaerobic            threshold intensities that is commonly around 80% VO2max)    -   g) Determining total anaerobic Training Effect by scaling        aerobic and anaerobic EPOC values and optionally their        derivatives. Training effect classification may be based on        commonly known coaching science, i.e. anaerobic work quantities        in different exercises. In addition, physical fitness level of a        person may be taken into account when evaluating the anaerobic        load of the performed exercise. In principle, a person with        higher fitness level (or activity level) needs to get higher        EPOC to achieve similar training effect;        -   In similar fashion, the performed aerobic EPOC is calculated            and scaled during exercise by comparing measured aerobic            EPOC to reference values for aerobic work. Person's physical            fitness level may be taken into account when classifying the            calculated EPOC. Classification may comprise aerobic            training effect having values typically between 1 and 5, and            having a verbal description between minor and overreaching            training effect.    -   h) Providing aerobic and anaerobic training effect values or        their proportions to the user    -   i) Although there is no actual interval detection method, it is        also possible to calculate time periods that exceed        predetermined limit values. For example, periods having modified        intensity higher than 100/o can be regarded as moderate        anaerobic intervals. Periods having modified intensity higher        than 115% can be regarded as high intensity anaerobic intervals.        Periods having modified intensity higher than 140% can be        regarded as high speed anaerobic intervals. These intensity        limits can be fixed but preferably they change linearly based on        user's fitness level.    -   j) When total anaerobic TE and the number of exercise periods        (=intervals bouts) above predetermined intensity values are        known (at any moment of exercise) it is possible to give        feedback sentences (See table 1 in FIG. 15 and table 2 in FIG.        16 ) regarding achieved exercise benefits.    -   k) Also other characteristics of different exercise periods can        be presented to the user, for example average duration and        intensity of intervals.

Implementation of calculation without having interval detection as amandatory step may not have as high requirements for calculationpower/memory. Therefore it may be more suitable to be used in commercialwristop computers or heart rate monitors. In addition it may allowbetter correspondence of results in an end user devices when similarexercises have been done with and without information on external workoutput—for example on one day user may perform interval workout outsidehaving GPS enabled whereas on another day he/she might perform theworkout inside on a treadmill. Of course, user expects that results aresimilar even if the input parameters used in calculation might bedifferent. In this exemplary embodiment modified intensity based modelmay be implemented in a way that it combines information on HR andexternal work output (GPS) to provide final estimate of intensity(modified intensity). Of course, HR based model works solely using HRinformation. Having HR information included in both models stabilizesresults: For example, results from treadmill workout (without speedinformation) correspond well with outside running results (with speedinformation). This approach may also stabilize results because both HRand external work output signals may always include error peaks even ifvarious artefact correction algorithms are applied. Averaging maycorrect error peaks on one part. In addition, model may be implementedin a way that boosting effect for external work output estimate(=modified intensity; can be calculated either solely based on heartrate or solely based on theoretical VO2 or by combining HR informationwith theoretical VO2 information) is applied only when both measuresshow similar trends: E.g. detected high speed peaks in GPS signal may beexcluded if HR trend does not show the same phenomenon or vice versa.

In one exemplary embodiment combination of aerobic and anaerobictraining effects is utilized in determining recovery time from exercise.Tables 3 and 4 show examples of how recovery time can be linked todifferent TE values. In one exemplary embodiment higher one of recoveryvalues is exposed to the user.

TABLE 3 Example of recovery time accumulation with respect to differentaerobic training effect values Recovery time Training Recovery timeTraining effect in hours effect in hours 1.0: 0.1 3.0: 19.4 1.1: 1.03.1: 20.4 1.2: 2.0 3.2: 21.3 1.3: 3.0 3.3: 22.3 1.4: 3.9 3.4: 23.3 1.5:4.9 3.5: 24.2 1.6: 5.9 3.6: 26.4 1.7: 6.8 3.7: 28.8 1.8: 7.8 3.8: 31.21.9: 8.8 3.9: 33.6 2.0: 9.7 4.0: 36.0 2.1. 10.7 4.1: 38.4 2.2: 11.7 4.2:40.8 2.3: 12.6 4.3: 43.2 2.4: 13.6 4.4: 45.6 2.5: 14.6 4.5: 48.0 2.6:15.5 4.6: 52.8 2.7: 16.5 4.7: 57.6 2.8: 17.5 4.8: 62.4 2.9: 18.4 4.9:67.2 5.0: 72.0

TABLE 4 Example of recovery time accumulation matrix with respect todifferent anaerobic training effect values and detected intensities.Anaerobic TE 1.0-1.4 1.5-1.9 2.0-2.9 3.0-3.9 4.0-4.9 5.0 No otherconditions Rec time Rec time Rec time Rec time Rec time Rec time 0-11 h12-23 h 24-47 h 48-71 h 72-95 h 96 h High speed or power Rec time Rectime Rec time Rec time Rec time Rec time detected 0-17 h 17-35 h 36-59 h60-81 h 82-95 h 96 h in several repeats Moderate anaerobic Rec time Rectime Rec time Rec time Rec time Rec time exertion detected in 0-11 h12-23 h 24-47 h 48-71 h 72-95 h 96 h several repeats Easy anaerobic Rectime Rec time Rec time Rec time Rec time Rec time exertion detected in0-11 h 12-23 h 24-47 h 48-71 h 72-95 h 96 h several repeats

In one exemplary embodiment anaerobic recovery time is calculated as afunction of anaerobic TE value (see table 5). In addition to that highspeed periods may be weighted in a way that they may boost recovery timeupwards with additional recovery time

Additional recovery time may accumulate as follows:

Additional recovery time in minutes=10*time over 140%VO2max(sec)+3.33*time over 115%VO2 max(sec)

In one exemplary embodiment maximum additional recovery time is 24 h.Accordingly, an exercise with 3.0 aerobic training effect, 3.0 anaerobictraining effect and 150 second of exercise above 140*% VO2max wouldproduce 25.8 h+24 h=49,8 h of recovery time.

TABLE 5 Example of recovery time accumulation with respect to differentanaerobic training effect values. Anaerobic Anaerobic AnaerobicAnaerobic Training recovery Training recovery Effect time in hoursEffect time in hours 0-0.9 0 3 25.8 1   0.1 3.1 27.1 1.1 1.4 3.2 28.41.2 2.7 3.3 29.7 1.3 3.9 3.4 30.9 1.4 5.2 3.5 32.2 1.5 6.5 3.6 35.1 1.67.8 3.7 38.3 1.7 9.1 3.8 41.5 1.8 10.4 3.9 44.7 1.9 11.7 4 47.9 2   12.94.1 51.1 2.1 14.2 4.2 54.3 2.2 15.5 4.3 57.5 2.3 16.8 4.4 60.6 2.4 18.14.5 63.8 2.5 19.4 4.6 70.2 2.6 20.7 4.7 76.6 2.7 21.9 4.8 83.0 2.8 23.24.9 89.4 2.9 24.5 5 95.8

In one exemplary embodiment the described invention is applied duringautomatically guided workouts where user exercises with a wristopcomputer, mobile phone or other similar device. In such a case user mayselect a target training effect for the workout or the target isselected automatically from e.g. a training plan. During the workoutguidance is given to the user by utilizing either auditory (voiceguidance), visual (guidance using text, pictures or symbols) orkinesthetic (vibration) feedback. The content of feedback helps the userin reaching the target in a comfortable way. In addition to targettraining effect, also training duration and/or distance can be preset.Exercise bank can be utilized in the way that several different exercisetypes are optional to the user: for example steady pace exercises, longintervals, and short intervals.

FIG. 3 presents an exercise having a plurality of intensity intervals.Usually a starting edge d1 (rising derivative), amount a, falling edged2 (falling derivative) and duration 1 of a intensity interval areclearly visible in a HR/time-chart. These are characteristics of thatintensity interval. A straightforward manner to determine the anaerobictraining effect achieved during the exercise is to determine eachinterval with its starting and ending points and its intensity as wellas duration by using memory buffer during recorded exercise. Afterdetection of these parameters anaerobic training effect can bedetermined by utilizing information on interval duration as well asdifferent weighting methods described in FIG. 4 . However, that kind ofcalculation would need still a lot of hardware resources.

FIG. 12 presents results of an intelligent calculation for anaerobicintensity in an exercise using minimum amount of memory. An ordinarytraining effect TE is calculated as taught in U.S. Pat. No. 7,192,401 B2which is incorporated herein. Intensity is monitored by a heart ratesensor and preferably by another sensor sensing output power, likespeed. The ordinary training effect TE (may be in terms of EPOC) isdetermined in a known manner. This disclosure presents now a method fordetermining an anaerobic training effect in same terms.

Referring to FIG. 12 the exercise has six intervals during 16 minutes,each interval lasting about one minute. Intensity (% VO2max), line 2 hasbeen measured indirectly from heartbeat signal. External workload, herespeed has been monitored by GPS. The ordinary intensity, line 2 givesquite a near repeating curve. Then measuring speed is much morechallenging, when there are breaks in signal. Only the third intervalhas a clear curve of the speed corresponding to the actual workout. Inall other intervals GPS-signal has been broken. However, whenever speedinformation is available, it may precede intensity data based on heartrate when calculating modified intensity. Thus, in the third intervalthe curves of speed and modified intensity coincide.

Another embodiment is shown in FIG. 14 .

Modified intensity is determined using an ordinary HR derived % VO2maxestimate, % HRmax and external workload after every 5 seconds with arange between e.g. 85-200%. Modified intensity is then converted to theaccumulation of anaerobic training effect (anTE) using an empiricfunction shown graphically in FIG. 13 . It counts the anTE adding eachnew value of a five second window to a sum, which presents a totalanaerobic training effect, but without scaling. Generally, thecalculation of modified intensity takes place in short periods of 2-20seconds, while a 3-10 second period may be better.

The intelligence of the above described method is based on minimuminformation about characteristics of each interval and using just acalculation window without full history data of exercise. Thecharacteristics of each interval is revealed just by a derivate ofintensity and a starting level, and intensity change calculatedpreferably in a simple manner as a continuous average value. The fullcharacteristics of each exercise interval are never revealed, butnecessary information of each interval is obtained indirectly in acontinuous calculation.

Referring to FIG. 14 heart rate (RRt) and external workload like speed Vis measured periodically, e.g. in 5 second periods (generally 0.5-15second, preferably 1-6 seconds). There is background information enteredinitially, particularly values depicting the maximum heart rate and thefitness level of the user,

The modified intensity is determined by a multiplication of a factorG_(t) and the measured intensity, i.e. the ordinary intensity. Thefactor G_(t) is calculated continuously, and it has initial value of oneas long as a gradient function yields a higher value over 1 (100%) usingboth the increasing gradient value and a starting level, step 44. Theillustration of FIG. 14 is schematic. Artefact corrections are notpresented. When the signal quality is weakening, the modified intensityis calculated more conservatively i.e. the factor G is reduced.

The level at which intensity ends up at any measurement point can betaken into account for example by using weights for multiplication ofthe actual gradient. A basic level of 50% yields weight of 0.35, 85%gives a weight of 1, 90% 1.12, and finally 100% level gives a weight of1.4. A gradient value is easily obtained as a difference of twosequential values (time difference always 5 seconds). The weight isfurther multiplied by the MET-difference between current and previousmeasurement point. For example, if intensity ends up to 85% level andhas increased by 2METs from previous point then G-value is 1.00×2-2.00.

A new value is calculated for the factor G_(t) in every period (step46). If the new value is bigger, an anaerobicTE-speed can be measuredimminently, step 48. Otherwise there is a deduction process decrease thevalue of the factor G until it is one. The deduction are based ondecreasing intensity, decreasing heart rate and/or decreasing externalworkload, step 50 Thus, another aspect is that factor G is kept up untilit is gradually reduced due to several different factors like decreasingintensity (step 50), decreasing heart rate and/or decreasing externalworkload. Preferably speed or other power output is measured, when thatinformation precedes heart rate based-intensity. After the deductionphase, step 50, the modified intensity (Mod) is calculated first in step48. The function in FIG. 13 yields the accumulation speed of theanaerobic training effect, f(Mod), particularly its upslope component.Unscaled anaerobic training effect (anaerobic TE₄) is obtained byintegrating all values to a sum. The recovery (a downslope component) isomitted and it can be handled in a similar manner as related to theaerobic TE (U.S. Pat. No. 7,192,401 B2) herein incorporated), ifdesired.

The “modified intensity” method according to FIG. 14 with recoverycalculation (downslope component) has an advantage of simplicity, whenthe aerobic training effect is calculated using dependency between anintensity/oxygen consumption and EPOC. Now a special calculation ofheart rate or measuring of external work load reveals actual andtemporal oxygen requirement of exercise (theoretical VO2), which meansthat temporally physiological intensity (sum of aerobic and anaerobicenergy yield) can be far over 100% VO2MAX—here limited TO 200% in thisexemplary embodiment. Using same FIG. 13 (or similar chart) both aerobicintensity and anaerobic intensity can be converted to momentary EPOC(“oxygen debt”)—values being so called upslope components. Whendownslope component is same for both, the overall calculation needrelatively little resources in addition to the ordinary TE-calculation.

In order to standardize the result being compatible to different sportand different number of intervals, step 52, the result is scaled usingalso an ordinary training effect and the number of executed intervals.The scaling can be accomplished in many ways.

Finally the result is displayed in step 54, when both ordinary trainingeffect and anaerobic training effect are shown in a display.

In one exemplary embodiment modified intensity is calculated as:

modified intensity=Anaerobic Multiplier(G)*intensity_t, where

anaerobic multiplier(G _(t))=1.3841*intensity_t{circumflex over( )}2*(MET_t-MET_t-1) and intensity_t is provided as %VO2 max.

The anaerobic multiplier (G_(t)) is based, in each period on finalmaximum intensity in selected power of range 1-4 (typically 2) and anincrease of intensity within the period.

If external workload (Speed & altitude or power output) is recorded itmay be heavily weighted in the calculation of modified intensity. In oneexemplary embodiment modified intensity may be calculated solely basedon external workload iffollowing conditions are fulfilled: Anaerobicmultiplier is greater than 1 and external workload is between 100 and200% VO2max.

In one exemplary embodiment modified intensity may be downgraded incases when recorded intensity has been continuously high. For example incases when intensity has not been under 70% VO2max any time in preceding5 min period. This rule may be used as a “sanity check” for the modifiedintensity especially in cases when external workload information is notavailable since it is impossible to perform significant amount ofanaerobic work if there are no recovery breaks during in short termhistory.

A practical example of calculation of modified intensity and EPOC whenexternal workload data is not available:

A Runner has VO2max of 52.5 ml/kg min which is equivalent to 15 METs(=his VO2max is 15-fold when compared to his expected resting VO2 of 3.5ml/kg/min). The runner starts an exercise during which he runs 100 mrepeats. In this example EPOC/TE accumulation is described in detailregarding the first repeat that lasts 15 seconds:

-   -   During the first 5 seconds ordinary heart rate based intensity        increases from 30% VO2max to 50%, which corresponds to an        increase in METs from 4,5 to 7,5MET. So the increase is 3METs        and correspondingly        -   anaerobic multiplier (G)=1.3841*0.5{circumflex over            ( )}2*3=1.03        -   modified intensity=1.03*50% VO2max=52% VO2max        -   Because modified intensity is lower than 85%, 50% intensity            is returned.            -   In this exemplary embodiment only intensities above 85%                VO2max are regarded meaningful in accumulating anaerobic                training effect        -   anaerobic EPOC accumulates by 0.1002 ml/kg        -   aerobic EPOC accumulates by 0.1002 ml/kg    -   During second 5 sec period intensity increase from 50% VO2max to        70% VO2max, which corresponds to an increase in METs from 7.5 to        105MET. So the increase is 3METs and correspondingly        -   Anaerobic multiplier=1.3841*0.7{circumflex over ( )}2*3=2.03        -   modified intensity=2.03*70% VO2max=142% VO2max        -   anaerobic EPOC accumulates from 0.1002 ml/kg to 4.1352 ml/kg        -   aerobic EPOC accumulates from 0.1002 ml/kg to 0.3967 ml/kg    -   During third 5 sec period intensity increases from 70% VO2max to        80% VO2max, which corresponds to an increase in METs from 10.5        to 12 METs. So the increase is 1,5 METs and correspondingly        -   anaerobic multiplier=1.3841*0.8{circumflex over            ( )}2*1.5-1.33        -   modified intensity=1.33*80% VO2max=106% VO2max        -   anaerobic EPOC accumulates from 4.1352 ml/kg to 5.5195 ml/kg        -   aerobic EPOC accumulates from 0.3967 ml/kg to 0.8738 ml/kg

Total accumulated EPOCs are: anaerobic EPOC—5.5195 ml/kg/min, aerobicEPOC 0.8738 ml/kg/min. Difference is 4.6457 ml/kg/min which would meanaccumulated anaerobic training effect value of 1.2 after the firstrepeat when runner's activity class is 7. When EPOC is used as ananaerobic sum measure scaling logic of FIG. 5 cannot be used as such.One suitable scaling logic is disclosed in patent publication U.S. Pat.No. 7,805,186 (B2) which presents an exemplary dependency between EPOC,activity class and Training effect.

In one exemplary embodiment, a method and system for detecting exerciseintervals according to the present invention may include defining theinterval-likeness of a time sequence of a physiological parameter,wherein the interval-likeness is proportional to at least some of thefollowing properties of the time sequence: the time derivatives withinthe sequence, the local minima and maxima within the sequence, and thefluctuations within the sequence. A time sequence may then be regardedas an exercise interval if its interval-likeness value is higher than apredetermined threshold value.

In further exemplary embodiments, methods and systems for detectingexercise intervals, analyzing anaerobic exercise periods, and analyzingtraining effects may be described. A physiological response of a usermay be continuously measured through one or more physiologicalparameters, wherein the physiological parameters may be recorded asphysiological values. One or more high intensity intervals andnon-interval periods may be identified based on a degree of change ofone or more of the physiological values over a period of time. Ananaerobic sum may be defined from at least one of; high intensityintervals and non-interval periods based on their properties. Ananaerobic training effect may be determined based on anaerobic sum and auser's background parameters. The anaerobic training effect may bedisplayed to the user in comparison with calculated aerobic trainingeffect.

As would be understood by a person of ordinary skill in the art, thetraining effect may be displayed in any manner as would be understood bya person or ordinary skill in the art. In further exemplary embodiments,the number of identified high intensity intervals, the duration of theintervals, or the like may be displayed to the user. In furtherexemplary embodiments, high intensity intervals may be classified andpresented to a user according to predetermined criteria in any manner aswould be understood in the art. In further exemplary embodiments, adescription of the exercise and the physiological effect may be providedin any manner as would be understood by a person of ordinary skill inthe art.

In further exemplary embodiments, methods and systems for analyzinganaerobic exercise periods, and analyzing training effects may bedisclosed. A physiological response of a user may be continuouslymeasured through one or more physiological parameters, wherein thephysiological parameters may be recorded as physiological values. Anexternal workload may be continuously measured wherein a plurality ofmeasured workload values may be recorded and each measured workloadvalue may be associated with one or more of the measured physiologicalvalues to form a plurality of data points. An aerobic training load maybe calculated based on a measured physiological response. An anaerobictraining load may be calculated based on measured external workload. Atotal training effect is determined using the higher training load valueand one or more of user's background parameters, and determininganaerobic training effect as a relative value according to comparisonbetween anaerobic training effect and total training effect.

In further exemplary embodiments, high intensity intervals may beidentified based on at least one of: measured physiological response andmeasured external workload. The high intensity intervals may beclassified based on predetermined criteria, and the number of identifiedhigh intensity intervals, duration of the intervals, and theclassification of high intensity intervals may be displayed to the user.In further exemplary embodiments, a description of the exercise and thephysiological effect may be provided in any manner as would beunderstood by a person of ordinary skill in the art.

In still further exemplary embodiments, methods and systems fordetecting exercise intervals, analyzing anaerobic exercise periods, andanalyzing training effects may be disclosed. A physiological response ofa user may be continuously measured through one or more physiologicalparameters, wherein the physiological parameters may be recorded asphysiological values. An external workload may be continuously measured,wherein measured workload values may be recorded and each measuredworkload value may be associated with one or more of the measuredphysiological values to form a plurality of data points. One or morehigh intensity intervals may be identified based on a degree of changeof one or more of the physiological and/or external workload values overa period of time. One or more identified high intensity intervals may bedetermined to be an anaerobic interval based on one or more factors. Ananaerobic sum of the one or more anaerobic intervals may be determined,and anaerobic training effect may be determined by comparing theanaerobic sum with an anaerobic work scale.

In still further exemplary embodiments, methods and systems fordetecting exercise intervals, analyzing anaerobic exercise periods, andanalyzing training effects may be disclosed. A physiological response ofa user may be continuously measured through a plurality of physiologicalparameters, wherein the plurality of physiological parameters may berecorded as physiological values. An external workload may becontinuously measured, wherein a plurality of measured workload valuesmay be recorded, and each measured workload value may be associated withone or more of the measured physiological values to form a plurality ofdata points. One or more data points may be filtered based onpredetermined criteria to form a plurality of accepted data points. Oneor more high intensity intervals may be identified based on a degree ofchange of one or more of the physiological or external workload valuesover a period of time. A probability that the one or more identifiedhigh intensity intervals is an anaerobic interval may be calculatedbased on one or more factors. The one or more high intensity intervalsmay be classified as an anaerobic interval if the calculated probabilityis above a predetermined threshold. An anaerobic sum of the one or moreanaerobic intervals and the anaerobic sum of non-interval periods may bedefined. An aerobic sum of the aerobic intervals may also be defined.Anaerobic training effect may be determined by comparing the anaerobicsum with an anaerobic work scale and an aerobic training effect may bedetermined by comparing aerobic sum with aerobic work scale. A totaltraining effect may be determined as being the higher training effectvalue, and a ratio between the anaerobic training effect and the aerobictraining effect may be determined, the ratio may represent theproportional benefit of exercise on, for example, energy productionpathways.

A singular training effect values for each individual workout may onlytell the immediate effect of one particular workout. That particulareffect is limited to a numerical value, showing that it was an easier orharder aerobic or anaerobic activity. While it is advantageous to beable to distinguish between aerobic and anaerobic workouts, there are,however, further dimensions to aerobic and anaerobic activity.

An aerobic activity that achieves a high training effect value may beachieved by performing a long, low intensity aerobic activity or mayalso be achieve by performing shorter, high-intensity aerobic VO2Maxintervals. In which case, it is helpful to further understand the exacttype of workout being performed, based not only on training effect buton other unique characteristics of a workout. Similarly, a very intenseanaerobic workout may focus on pure anaerobic power, which may becharacterized by short bursts of activity with long rest, or onanaerobic capacity, using workouts that feature relatively shorter rest.

Possessing this kind of information may assist exercisers in betterunderstanding the specific type of fitness each workout may haveimproved. Over a longer term, it would also be possible to see thedistribution of the different types of training and to know if there hasbeen adequate balance between the various types of training or whether aspecific dimension of fitness has received sufficient attention.

Furthermore, information about the training intensity distribution(training load distribution) can be used to help a user to betterunderstand his/her current training status. (See applicants' priorpatent applications US2018/0174685A1 and US2018/0310874A1). For example,if a user's training load is moderate but fitness level is declining,the user's training status may be determined to be “Unproductive”. Byanalyzing the activity history and determining training loaddistribution the reason behind “unproductive” status may be solved. Ifthe training history shows a clear shortage in aerobic low intensitytraining, it would be easy to tell the user e.g. “‘Unproductive’—Highrelative amount of high intensity training may have turned your trainingstatus into Unproductive. Increase aerobic low intensity training to getProductive again”. If information about training load distribution wouldnot be analyzed at all, there would also be likelihood that anevaluation is made that the quality of training has been OK and thatdeclining fitness level may be caused by poor recovery induced by poornutrition, poor sleep, excess stress or illness. On the other hand, theabove example history data analysis may also show a good balance interms of training intensities in which case user should be instructed tocheck his/her other living habits or health status as they would then bethe likely reasons behind poor fitness development. In other words, thecurrent method enables, for example, a fitness device to automaticallyexclude certain reasons and thus enable more accurate instructions forfuture. Of course, in the above example history data analysis may alsoshow a good balance in terms of training intensities in which case usershould be instructed to check his/her other living habits or healthstatus.

In an exemplary embodiment, a method may be used to provide additionalfeedback on the specific benefit of each exercise session. The resultsof the workout may be supplemented by additional feedback beyond showinga singular training effect value. The additional feedback, which may bereferred to as a “training label”, may be determined using the stepsshown in FIGS. 17-19 . A first part of the process, as describedpreviously, is to determine a numerical value of training effect, forboth an aerobic training effect and anaerobic training effect.

In an exemplary embodiment, a determination of feedback phrase is madeseparately for both the aerobic training effect (FIGS. 18, 19, 21 ) andanaerobic training effect (FIGS. 15-17, 19 ).

Aerobic Feedback

Aerobic feedback phrases may be determined based on the aerobic trainingeffect value and information on the intensity distribution of exercise.In running intensity distribution is analyzed in terms of heart rate andoptionally running speed and in cycling in terms of heart rate andoptionally cycling power (see FIG. 18 ).

As can be seen from FIG. 18 , analysis of intensity distribution may bebased on various factors related to the intensity a person exercises atand the length of time spent in certain intensity zones (in terms of,for example, heart rate, running speed, or cycling power), or theduration of the period of intensity relative to the total duration ofthe activity.

Intensity may be measured as a relative intensity, benchmarked againstlactate threshold heart rate (LTHR) or lactate threshold speed (LTspeed) or Functional Threshold Power (FTP) of the user. These measuresmay be manually input by the user or automatically calculated based onearlier workouts. If the values are not known by other means, they mayalso be estimated using the following formulas:

In an alternative embodiment, the results of the workout may besupplemented by additional feedback beyond showing training effect, andis described in several steps below.

Showing a numerical value of training effect value for:

-   -   a) Aerobic training Effect    -   b) Anaerobic Training Effect

Determining and showing feedback phrases related to both aerobic andanaerobic work to the user (FIG. 15-21 ).

Aerobic Feedback phrase telling the aerobic benefit of workout (FIGS. 18, & 19 and 21). Aerobic feedback phrases may be determined initiallybased on the aerobic training effect value, and may additionally bechosen based on extended criteria. Extended criteria is based onrelative intensity wherein the intensity is benchmarked against lactatethreshold heart rate (LTHR) or lactate threshold speed (LT speed) orFunctional Threshold Power (FTP) of the user, and examples of saidcriteria are shown in FIG. 18 . In cases where these values are notknown they may be estimated using the following formulas

Est. FTP power(W)=((maxMET*3.5*weight-350)/12.24)*0.828

Est. LT speed(m/s)=(maxMET*3.5-3.5)/12*0.828+0.1486

If LTHR not known, use default 90%Hr max as LTHIR

where maxMET may be determined based on a user's fitness level, orVO2Max.

Some of the criteria may also be based on the corresponding anaerobictraining effect value.

FIG. 17 (corresponding to Table 5-1) presents an example table of analternative embodiment for the selection of anaerobic feedback and FIG.18 (corresponding to Table 5-2) presents an example table of analternative embodiment for the selection of aerobic feedback.

TABLE 5-1 ANAEROBIC FEEDBACK PHRASE LOGIC Anaerobic TE Other conditions0-0.9 1.0-1.9 2.0-2.9 3.0-3.9 4.0-4.9 5.0 Only anaerobic phrases 0-4 and15 are possible when ″gym″ flag is selected for a workout Other rulesrelated to use of ETE library: Speed inputted to ETE only in runningexercises. Power inputted to ETE only in cycling workouts Power No otherconditions #0 #15 #1 #2 #3 #4 c1 Peak modified intensity >140% #0 #5 #6#12 #13 #4 Speed in 5 or more repeats (scale 12-17 MET => 150%-130%) ANDeach repeat lasting 20 sec or less c2 Average mod intensity #0 #15 #7 #8#3 #4 Power >115% for a total of 75 sec or more AND in 3 or more repeatsAND each repeat lasting 10 sec or more (scale 12-17 MET => 120%-110% c3Average mod intensity >95% #0 #15 #10 #11 #14 #4 Power for a total of150 sec or more AND In 7 or more repeats (scale 12-17 MET => 100%-95%)AND each repeat lasting 20 sec or more AND aerTE < 4.0

TABLE 5-2 AEROBIC FEEDBACK PHRASE LOGIC Aerobic TE Other conditions:0-0.9 1 1.5 2 2.5 3 3.5 4 4:5 5 Recovery/ No other conditions #18 #0 #1#2 #3 #4 Tempo c1: Recovery/ 10 min ≤Duration ≤40 min AND anTE <1.0 #18#5 #1 #2 #3 #4 Tempo c2: Recovery/ Duration >40 min AND Time in HRzone >95% LTHR for #18 #0 #6 #7 #8 #9 #10 #11 Base less than 8% of totalduration AND C4 + 2 + C5 + 2 less than 15% of total duration AND anTE<4.0 AND time at 61-92% LTHR >35 min c3: Recovery/ Cumulative time #18#0 #21 #22 #23 #24 Tempo 1. @ HR zone 90-96% LTHR equal to or more than18-24 min AND cumulative time equal to or more than 20% of totalexercise time 2. OR ETE speed @ 90-96% of AnT speed ≥18-24 min ANDcumulative time equal to o more than 22% of total exercise time 3. OR 30sec_avg_power @ 75-92% of Antpower) ≥24-32 min AND ″interval notdetected″ AND cumulative time equal to or more than 25% of totalexercise time AND anTE <4.0 c4: Recovery/ Cumulative time #18 #0 #19 #12#13 #14 LT 1. @ 94-102% LTHR AND @ modified intensity <95% VO2max equalto or more than 10-15 min AND cumulative time equal to or more than 15%of total exercise time OR 2. @ ETEspeed 94-105% of AnT speed equal to ormore than 10-15 min AND cumulative time equal to or more than 15% oftotal exercise time OR 3. @ 30 sec_avg_power 90-105% of AnT power equalto or more than 13-18 min AND ″interval not detected″ AND cumulativetime equal to or more than 20% of total exercise time AND anTE <4.0 C5:Recovery/ if duration ≥5 min AND cumulative time at one of the zones #18#0 #20 #15 #16 #17 VO2max (see below; either based on % LTHR or % ETEspeed or % AnT power) in zone greater than 8 min or more than 80% oftotal duration else if.. Cumulative time 1. @ ≥100% LTHR AND modifiedintensity ≤103% equal to or more than 8-12 min OR 2. @ ETE speed ≥102%of AnT speed AND modified intensity ≤103% equal to or more than 8-12 minmin AND cumulative time equal to or more thatn 10% of total exercisetime OR 3. @ 30 sec_avg_power higher than 102% of AnT power AND smallerthan 127% of AnT power equal to or more than 8-12 min AND ″interval notdetected″ AND cumulative time equal to or more thatn 15% of totalexercise time.

Example Cases According to Tables 5-1 and 5-2:

A cyclist (AC8) performs a workout consisting of a variable intensitywarm-up, 2×20 min repeats at an intensity close to or above his lactatethreshold (FTP) intensity and a short cool-down. During a warm-up hisintensity is mostly lowish staying mostly below 90% LTHR and below 76%FTP. During the latter part of warm up his intensity increases above 76%FTP but that does not yet raise aerobic TE to 2.0. Since time atrecovery and base training zones (=61-92% HRmax) does not accumulateabove the 35 min threshold, and short recovery does not accumulate muchtime at Tempo zone or higher zones either, aerobic feedback phrasechanges from #18 (no aerobic effect) into #5 (Easy recovery). At thattime point no anaerobic training effect has yet accumulated and thusanaerobic feedback phrase stays at #0 (no anaerobic effect). Thus theprimary benefit after the warm-up is #1—“Recovery”. After a few minutesof first FTP-power repeat, while the aerobic TE reached the level of2.0, aerobic feedback phrase turns into #1 (Maintaining aerobic) whichtriggers also the change of primary label into “Aerobic base” since theexercise has still accumulated very little of high intensity effort whencompared to total working time (35 min) so far. Also the anaerobic TE isstill 0.0 at that point. However, about 5 minutes later, when repeat hastaken approx. 8 min. Aerobic TE reaches 3.0 level and accumulated timeat 61-92% HRmax—intensity is less than 35 min. Aerobic feedback phrase#8 is thus not possible and aerobic feedback phrase turns into #2(“Improving Aerobic”) which in turn changes primary label from “Base”into “Tempo” since anaerobic TE is still 0.0. At the end part of first20 min repeat the cyclist has accumulated enough time (13 min 20 sec)considering his activity class at lactate threshold HR zone (94-102%LTHR) without modified intensity simultaneously reaching too high values(>95%). Accordingly, aerobic feedback phrase changes into #12—“Improvinglactate threshold” which also causes the primary label to turn into#4—“Lactate Threshold”. After a short recovery period the user startshis second repeat where the intensity is slightly higher when comparedto the first one. During the second repeat aerobic feedback phrase turnsinto #13—“Highly improving lactate threshold” but it does not causechanges in primary label. Due to the increased effort time at VO2maxtraining zone starts to accumulate. Also, the anaerobic training effectis starting to increase. At the very end of 2^(nd) repeat user reaches atrigger limit which is 10 min 30 sec at VO2max HR zone. Accordingly,aerobic feedback phrase turn into #16—“Highly improving VO2max”. In thecomparison of aerobic training effect (4.5) and anaerobic trainingeffect (3.5) aerobic energy systems are regarded as the mainbeneficiary. Accordingly, primary label (primary benefit) at the end ofworkout is #5 (VO2max)-not anaerobic capacity even though the anaerobicwork of workout has focused on that ability. All aerobic load of thatexercise (208 units) is allocated to “VO2max”-label and “Aerobic high”category. All anaerobic load (116 units) is allocated to “Anaerobic”intensity category since the workout exceeded anTE 1.0 level. Anaerobicload is further allocated under “anaerobic capacity” label sincedetected supramaximal effort did not reach high enough level in order tobe regarded as speed training (Anaerobic feedback phrase=#2).

Anaerobic Feedback

Anaerobic feedback phrases tell the anaerobic benefit of workout,examples of the feedback are shown in FIGS. 17, 19, and 20 . Feedbackphrases may be determined using the determined anaerobic trainingeffect, in addition to criteria related to modified intensitymeasurements as well as quantity, duration, and intensity of detectedanaerobic intervals. This may also include external workload intensity,such as based on speed from running workouts or power input from cyclingworkouts.

Summary Feedback

Based on both the aerobic and anaerobic feedback phrases determined foreach exercise, a summary of the current training session may bedetermined by means of workout labels. The purpose of labels is tosummarize the benefits of workout with respect to the physiologicalsystems developed. Aerobic workout labels 1-5, comprise for example,recovery training, aerobic base training, tempo training, lactatethreshold training and VO2max training.

Anaerobic training labels 6-7, comprise for example anaerobic capacitytraining or speed training.

Summarizing the current training session by determining all of thebenefits (=load for

-   -   1 or more workout labels) of said training session        -   a. For aerobic energy production            -   i. Workout labels 1-5 (FIG. 19 ) comprising, for                example, recovery training, aerobic base training, tempo                training, LT training and VO2max training)        -   b. For anaerobic energy production (labels 6-7) (See FIG. 19            )            -   i. Comprising e.g. anaerobic capacity training and speed                training

In one exemplary embodiment presented in FIG. 19 each workout may gettwo labels (meaning two main benefits): 1) one of the aerobic low oraerobic high intensities comprising labels #1-#5 and 2) either one ofthe anaerobic labels #6-#7. For example, an easy jogging workoutincluding ten 50 m sprints may get label 2 (“Base”) from the aerobiclabels and label 7 “Sprint” from the anaerobic labels.

Determining and showing the primary benefit (primary label) of workout.From a coaching point of view it is often useful to also point out theprimary label (=primary benefit) of each workout. The selection of theprimary label of the workout may be performed based on the calculatedaerobic and anaerobic training effect using for example the followingcriteria:

-   -   When anaerobic TE (anTE) 3.0, select anaerobic label (label 6        or 7) as the primary label if it is greater or equal to aerobic        TE (aerTE)-0.5. If anTE <3.0, then the primary label is        whichever is higher, aerobic (label 1,2,3,4 or 5) or anaerobic        TE    -   If anTE=aerTE then the Anaerobic effect is always selected as        primary label    -   If anaerobic label is SPEED AND anTE≥2.0, select anaerobic label        as primary label if it is greater or equal to aerobic TE-1. If        anaerobic label is SPEED AND anTE <2.0, then the workout label        is based on whichever training effect is higher, aerobic or        anaerobic TE.    -   In “strength training mode”, select anaerobic label as workout        label if AnTE ≥1 if it is greater or equal to aerobic TE-1.5.        “Strength training” is an example of a mode may be selected        whenever user selects a sport mode for an exercise that is        characterized by lifting weights or where user has to use high        amount of force in short bouts.

There may optionally be presented a “secondary” label, which would bethe training effect label that is not selected as the primary label.

To illustrate the balance of a user's training, the training history maybe summarized. In an exemplary embodiment, a summarized training historymay comprise summing all of the training load accumulated anddistributed to the different training labels. The training load isdistributed unweighted, regardless of whether it has the “primary” or“secondary” training load (For example a training session with aerobicTE of 3.3 and anaerobic TE of 1.1 would get a primary label from some ofthe aerobic labels #1-#5. Regardless of that also the secondaryanaerobic effect #6 or #7 would be taken into account in loaddistribution). Any duration of historical training load may be presentedto the user. FIG. 22 shows an example of cumulative training loadfeedback. As shown in FIG. 22 , historical training load may be shown ina daily calendar, a weekly (7-day) illustration, a monthly (28-day)illustration, a custom date as specified by the user, or even a yearly(365-day) distribution (not shown). Load distribution may also be shownper each label or alternatively by diving labels e.g. into 3 groups (asshown in FIG. 19 ): Labels 1-2 to “Aerobic low intensity” category,labels 3-5 to “Aerobic High Intensity” category and labels 6-7 to“Anaerobic intensity” category.

The purpose of calculating the intensity-based load distribution is totrack whether a person is sufficiently stressing different body systems.

In a specific situation, where feedback phrase number 0, with theworkload label of NaN as shown on FIG. 20 may not be taken into accountin training load distribution.

Historical training load distribution may be calculated using adifferent software library (in this case Training History Analysis—THAlibrary) than the library that calculates aerobic and anaerobic TEvalues, feedback phrases and workout labels for a specific workout. Thismay help in saving computational power as these calculation processesneed not be performed at regular intervals (e.g. 5 sec intervals) butinstead, may be calculated only after a new exercise session or in thebeginning of a new day.

It is also possible to combine training status to training loaddistribution feedback (also referred to as training load balancefeedback). This feedback logic is also described below.

Training Load Distribution

To summarize training history in a more simplified manner, thedistribution of training load may be placed into intensity categories.Training load may be divided into three intensity categories (AerobicLow, Aerobic High and Anaerobic) based on the workout label of eachexercise. Aerobic low may generally be defined as low-intensity aerobictraining, for example, aerobic exercise at a heart rate below 80% of aperson's maximum heart rate. This kind of training forms the basis ofany endurance training plan as this type of training allows hightraining volumes. Aerobic high would then be considered exercise thatinvolves a higher heart rate than the defined intensity threshold ofaerobic low, but does not belong in the category of being an anaerobicexercise, anaerobic exercise being identified, for example, by themethod described above. Aerobic high training intensities can be used tooptimize aerobic capacity. However, regardless of being efficient inoptimizing aerobic (cardiorespiratory) capacity this kind of trainingincrease training load rapidly and can thus not be repeated as often asaerobic base workouts. Accordingly, Aerobic Low intensity trainingallows training on a daily basis (or even several daily workouts) inlong term which is why this type of training forms the basis ofendurance training. Anaerobic training is performed at intensitiesbeyond a person's VO2max. They are needed to optimize performance asthis kind of training improves, for example, exercise economy, as wellas capability to (repeated) sprints which are crucial characteristics inendurance sports.

Accordingly, all training intensities have are relevant when it comes todevelopment and optimization of endurance performance.

In addition to load sums for each intensity category, the THA librarymay also calculate load target areas for each intensity category, asshown in FIG. 22 .

Exemplary cumulative load targets per each intensity category shown inFIG. 22 may be determined for example using an athlete database whereinthe limits are based on averages and standard deviations observed insubgroup of athletes whose fitness level has been developing faster thangroup average. The limits may also take into account user's activityclass and are described in more detail below.

The feedback presented to a user may be presented on the apparatusdescribed earlier and shown in FIG. 11 . This feedback may be, forexample, based on the realized training load of the training history ascompared to the training load target values for each of theaforementioned categories. In addition to the graphical informationshown, additional feedback phrases and sentences, as shown in tables 8and 9 below may be presented to the user on the selected apparatus.

Activity Class “Basic” AC (Basic Activity Class)

The activity class refers to a general descriptor of a person's fitnesslevel, activity history or training history. The present activity classmay be evaluated using background information, such as age, gender, afitness level (e.g. a maximum oxygen consumption value, VO2max) and/ortraining history data. A target exertion level for each planned exerciseis determined individually for each person. The target exertion level ofa user may be determined based on maximum heart rate (maxHR), of a user,which may be determined based on age, which is received as a backgroundinformation. Other background information may also have an effect on thedetermination of an exertion level of a user. For example, fitness levelmay be estimated using background information optionally in combinationwith training history. Resulting fitness level may be used to determinetarget velocity, pace or power for different workouts. In a case where auser has performed exercise(s) by recording heart rate (HR) andpositions, e.g. GPS positions; or HR and external power, it is possibleto determine fitness level more accurately and thus the accuracy oftarget speeds and/or powers for workouts is determined more accuratelycorrespondingly. Fitness level may be determined as described in U.S.Pat. No. 9,237,868 and U.S. Ser. No. 10/123,730. Further, othermeasured, calculated, detected or estimated values may have effect onthe determination of an exertion level of a user. Measured values maycomprise heart rate and heart rate variability (HRV) of the user. Thus,training history of a user, if available, may comprise information thathas an effect on prescribed exertion level of a user. An activity classclassification may comprise a scale, e.g. of 0-10, wherein 0 representsa sedentary person, while 10 represents highly fit/trained user, whoexercises regularly. Each activity class has its own specific trainingload target. The training load target may comprise a range. The trainingload target comprises a lowest limit for a training load of the activityclass. In addition, the training load target may comprise an upper limitfor a training load of the activity class.

Person's training tolerance may, for example, be determined based onmonthly training load (28 day training load sum=monthly trainingload=MTL), VO2max, gender and age where age and gender can be used toclassify the VO2max value. Training load and VO2max may have separatecriteria and basic AC may be determined as a maximum of these two (seeFIG. 25 ). For example, a 40 year old man with VO2max of 45 ml/kg/minand MTL of 1600 would get an activity class of 6 based on his VO2max andactivity class of 8 based on MTL value. Thus his final “basic” activityclass would be 8.

Averaged Activity Class (aAC)

The training load target values for each category may be based on anaveraged activity class (aAC) which is more stable compared to only“basic” AC. Using only AC could lead to a “moving goalposts” issue,where a user who tries to balance their training load ends up in adifferent activity class with different MTL limits, and further awayfrom balance. For example, a user with a shortage of anaerobic trainingcould try to reach balance by doing a hard anaerobic workout, and thisworkout could then push the user to a higher AC and result in a shortageof aerobic load.

Similar to basic AC, aAC is a maximum of a VO2max-based value, a monthly(28-day) load—based value, and an activity class constant such as 4.0.However, to make aAC less sensitive to changes in the short-termtraining load, the monthly load is calculated as a weighted sum thatputs emphasis on how the user trained approximately a month ago. Thisaveraged monthly training load (aMTL) is calculated as follows:

-   -   1. Compute the daily average training load of the user as a        weighted average where each workout is weighted depending on its        age as shown in the table below (exercises on the current day        and 6 preceding days get a weight of 1, exercises in the        preceding 7 day period get a weight of 2, etc).    -   2. Scale the daily average training load to a monthly aMTL as in        the usual MTL (If the known training history spans N≤6 days,        multiply by 4*N, otherwise multiply by 28).

Computed this way, aMTL is approximately equal to an arithmetic averageof MTL as computed 0, 1, 2, 3, and 4 weeks ago.

By way of example, similar to basic AC, if 40-year-old man with 45ml/kg/min VO2max was able to achieve aMTL of 1600 units, also his aACwould then be 8. However, it is somewhat harder to reach 1600 units ofaMTL when compared to 1600 units of MTL

TABLE 7 Weighting of training load based on the date of exerciserelative to current date. Age of exercise (days) Weight 0-6 1  7-13 214-20 3 21-34 4 35-41 3 42-48 2 49-55 1 56-  0

Target behavior in aAC is to show load target level that has beensuitable target for the user during last month; i.e what user has beenseeing as the target weekly load during past month. It is thus changingmore slowly than “basic” AC meaning that “basic” AC may now have harderrequirements to the user for the coming week than what is therequirement for the past months training.

Determination of Training Load Targets, Based on Activity Class

Activity class (AC)-based target values for monthly training load (MTL)are determined for each category. As a general rule of thumb: trainingis in good balance when load in each category is within their targetlimits.

During an early phase of the training when the user has not yet trainedfor a full month (28 days) or there is not enough training data history,the target values shown below in table 6 are multiplied with U28, whereL<28 is the number of days from the oldest recorded exercise to thecurrent date.

TABLE 6 Sample training load target values TARGET VALUES Aerobic LowAerobic High Anaerobic Minimal limit 12.5% of MTL 3.0 15% of MTL 3.0  5%of MTL 3.0* Target lower   25% of MTL 3.0 30% of MTL 3.0 10% of MTL 3.0*limit Target upper   55% of MTL 3.0 60% of MTL 3.0 30% of MTL 3.0 limitVery high limit   80% of MTL 3.0 80% of MTL 3.0 60% of MTL 3.0 MTLlimits always determined based on monthly average activity class (aAC)*IF AC ≤ 7 minimal limit AND target lower limit for anaerobic is 0

Training Distribution Feedback

Based on the actual accumulated load and its distribution, exemplaryfeedback sentences are provided according to the rules described in thebelow tables 8 and 9.

There are some potential exceptions to the rules shown below:

-   -   Below targets: Monthly load is under 65% of MTL 3.0 (this value        is scaled during the onboarding phase like other training load        target values)        -   “Approaching targets” is provided instead of below targets            if WTL ≥2.5    -   Above targets: 145% or higher of MTL 3.0 (this value is        intentionally not scaled during the onboarding phase)        -   Focus is selected for that category which is proportionally            closest to “upper-limit” or “very high limit”    -   ” Additional exceptions would be to not allow 1) “Above targets”        NOR 2) “Approaching targets” if the below table suggests “#2        Aer. low Shortage” (I.e. Then show “#2 Aer. low Shortage” to a        user)

TABLE 8 Distribution # feedback Example of long feedback  0 No result  1Below “Your long term training load is below optimal. Increase trainingload” Targets **Rule: Monthly load is under 65% of MTL 3.0  2 Aerobic“Total amount of high intensity training is too high with respect toamount Low of low intensity training. Increase amount of low intensitytraining to get Shortage your training into better balance and todevelop performance optimally”  3 Aerobic “Proportion of aerobic highintensity training has been very low during High past 4 week period.Training may not therefore develop aerobic capacity Shortage (LT/VO2max)optimally”  4 Anaerobic “Proportion of anaerobic high intensity traininghas been very low during Shortage past 4 week period. As variation intraining stimuli per se and impact for anaerobic capacity and speed hasbeen too low - training benefits is expected to be suboptimal”  5Balanced “Your training is in good balance thus expectedly resulting incomprehensive benefit for all areas of fitness”  6 Aerobic “Yourtraining focus has been in aerobic low intensity training. This type LowFocus of focus builds foundation for sustaining harder trainingperiods.”  7 Aerobic “Your training focus has been aerobic highintensity training. This type of High Focus focus typically rapidlyimproves LT, VO2max and endurance performance.”  8 Anaerobic “Yourtraining focus has been anaerobic high intensity training. This typeFocus of focus maximizes endurance performance rapidly. Notice that yourbody needs aerobic training focus after this kind of training period”. 9 Above Your training load is high. Remember to include lightertraining periods targets, Aer. to your training schedule. Low Focus**Rule: Focus is selected for that category which is proportionallyclosest 10 Above to “upper-limit” or “very high limit”. targets, Aer.High Focus 11 Above targets, Anaerobic Focus 12 Approaching “Your longterm training load is below optimal but is moving towards targets**targets” **Rule: “Approaching target” is provided instead if “belowtargets” if WTL ≥ 2.5

It may be considered obvious to a man skilled in art that the tableabove could be modified in scope of this invention. For example.Distribution feedbacks #0-8 could form a basic set of feedbacks.Additionally, feedbacks 9-11 could be combined under a generic “Abovetargets” feedback. Furthermore, feedback #12 “Approaching targets” canbe included if a more positive “tone of voice” is preferred instead ofcorrective feedback.

Table 9 shows the feedback phrase selection logic based on therelationship between aerobic training load and its related traininglimits, and the anaerobic training load and its related training limits.

In addition to the typical feedback, some cells in the below table 9include additional feedback, specifically “(Below targets)” and “˜(Abovetargets)”. These references may be optionally used to overrule theprimary feedback based on the MTL based rules presented in the priortable. Hence, in certain situations, an additional feedback or rule maybe included to overrule the initial rule in particular circumstances.

TABLE 9 TRAINING LOAD DISTRIBUTION FEEDBACK PHRASE LOGIC Aer. High Aer.below Aer. High Aer High low below minimal below Aer. High Aer. Highabove Very minimal limit limit target in target above target high limitAnaerobic #5 Balanced #2 Aer. Low #2 Aer. #2 Aer. low #2 Aer. low below(#1 Below Shortage low shortage shortage minimal limit targets) (#1Below shortage targets) (#1 Below targets) Anaerobic #2 Aer. Low #2 Aer.Low #2 Aer. #2 Aer. low #2 Aer. low below target Shortage Shortage lowshortage shortage (#1 Below (#1 Below shortage targets) targets)Anaerobic in #2 Aer. Low #2 Aer. low #2 Aer. #2 Aer. low #2 Aer. lowtarget Shortage shortage low shortage shortage (#1 Below shortagetargets) Anaerobic #2 Aer. low #2 Aer. low #2 Aer. #2 Aer. low #2 Aer.low above target shortage shortage low shortage shortage (#1 Belowshortage targets) Anaerobic #2 Aer. low #2 Aer. low #2 Aer. #2 Aer. low#2 Aer. low above very shortage shortage low shortage shortage highlimit shortage (Above targets) Aer. High below Aer. High Aer High Aer.low minimal below Aer. High Aer. High above Very below target limittarget in target above target high limit Anaerobic #6 Aer. Low #5Balanced #4 #2 Aer. low #2 Aer. low below Focus (#1 Below Anaerobicshortage shortage minimal limit (#1 Below targets) Shortage targets)Anaerobic #5 Balanced #2 Aer. Low #2 Aer. #2 Aer. low #2 Aer. low belowtarget (#1 Below Shortage low shortage shortage targets) (#1 Belowshortage targets) Anaerobic in #3 Aer. high #2 Aer. low #2 Aer. #2 Aer.low #2 Aer. low target Shortage shortage low shortage shortage (#1 Belowshortage targets) Anaerobic #3 Aer. high #2 Aer. low #2 Aer. #2 Aer. low#2 Aer. low above target Shortage shortage low shortage shortageshortage Anaerobic #3 Aer. high #2 Aer. low #2 Aer. #2 Aer. low #2 Aer.low above very Shortage shortage low shortage shortage high limitshortage (Above targets) Aer. High below Aer. High Aer High Aer. low inminimal below Aer. High Aer. High above Very target limit target intarget above target high limit Anaerobic #3 Aer. high #4 #4 #4 Anaerobic#4 Anaerobic below Shortage Anaerobic Anaerobic Shortage Shortageminimal limit (#1 Below Shortage Shortage targets) (#1 Below targets)Anaerobic #3 Aer. high #3 Aer. high #5 #7 Aer. High #7 Aer. High belowtarget Shortage Shortage Balanced Focus Focus (#1 Below targets)Anaerobic in #3 Aer. high #5 Balanced #5 #7 Aer. High #7 Aer. Hightarget Shortage Balanced Focus Focus Anaerobic #3 Aer. high #8 #8 #7Aer. High #7 Aer. High above target Shortage Anaerobic Anaerobic FocusFocus Focus Focus (Above targets) Anaerobic #3 Aer. high #8 #8 #8Anaerobic #8 Anaerobic above very Shortage Anaerobic Anaerobic FocusFocus high limit Focus Focus (Above (Above targets) targets) Aer. Highbelow Aer. High Aer High Aer. low minimal below Aer. High Aer. Highabove Very above target limit target in target above target high limitAnaerobic #3 Aer. High #4 #4 #4 Anaerobic #7 Aer. High below ShortageAnaerobic Anaerobic Shortage Focus minimal limit (#1 Below shortageShortage (Above targets) targets) Anaerobic #3 Aer. High #6 Aer. Low #6Aer. #5 Balanced #7 Aer. High below target Shortage Focus Low FocusFocus (Above targets) Anaerobic in #3 Aer. High #6 Aer. Low #6 Aer. #5Balanced #7 Aer. High target Shortage Focus Low Focus Focus (Abovetargets) Anaerobic #3 Aer. High #6 Aer. Low #6 Aer. #2 Balanced #7 Aer.High above target Shortage Focus Low (Above Focus Focus targets) (Abovetargets) Anaerobic #3 Aer. High #8 #8 #8 Anaerobic #8 Anaerobic abovevery Shortage Anaerobic Anaerobic Focus Focus high limit Focus Focus(Above (Above targets) (Above targets) targets) Aer. High Aer. low belowAer. High Aer High above very minimal below Aer. High Aer. High aboveVery high limit limit target in target above target high limit Anaerobic#3 Aer. High #4 #4 #4 Anaerobic #3 Aer. Low below Shortage AnaerobicAnaerobic Shortage focus minimal limit Shortage Shortage (Above (Abovetargets) targets) Anaerobic #3 Aer. High #6 Aer. Low #6 Aer. #6 Aer. Low#6 Aer. Low below target Shortage Focus Low Focus Focus Focus (Above(Above targets) (Above targets) targets) Anaerobic in #3 Aer. High #6Aer. Low #6 Aer. #6 Aer. Low #6 Aer. Low target Shortage Focus Low FocusFocus Focus (Above (Above targets) (Above targets) targets) Anaerobic #3Aer. High #6 Aer. Low #6 Aer. #6 Aer. Low #7 Aer. High above targetShortage Focus Low Focus Focus (Above Focus (Above (Above targets)targets) (Above targets) targets) Anaerobic #3 Aer. High #8 #8 #8Anaerobic #8 Anaerobic above very Shortage Anaerobic Anaerobic FocusFocus high limit (Above Focus Focus (Above (Above targets) targets)(Above (Above targets) targets) targets)

It may also be considered obvious for a person skilled in the art thatthe above presented 5×5×5 “decision cube” could be replaced with a moresimple logic, for example using a 3×3×3 logic cube where each intensitycategory may be determined with 3-level scale: 1) below target 2) intarget and 3) above target. The embodiment represented in the tableabove, including the 5-level scaling where each level may be asfollows-1) below minimal limit 2) above minimal limit but below target3) in target, 4) above target and 5) above very high limit—may enablemore precise feedback while a more simple system may be easier tovisualize in devices having small displays where additional limits maynot fit that well to the device display.

Training Status Feedback Phrases

In an embodiment, combining different training load distributionfeedback phrases with different training statuses enables more advanced,versatile feedback phrases than just a single training status. Theseadvanced training status feedback may provide an explanation of theprevailing training status and may be beneficial for the planning ofupcoming exercise sessions.

Within the THA analysis library, training_status_feedback_phraseinterprets the current training status as it relates to the currenttraining load distribution. For example, this may become useful if thereis some element in the training load distribution that may explaincurrent status. One example situation may be, for instance, if there isa poor training status (for example, unproductive), which may beexplained by an unbalanced load distribution. Alternatively, there maybe situations where training status is generally acceptable but thereare improvements possible by adjusting the training load balance. Table10 below shows the relationship between some example training statuses,similar to those described in applicant's earlier patent applicationUJS20180174685, as they relate to some training load distributionlabels. Table 11 shows example feedback phrases corresponding to thelogic number determined in Table 10.

TABLE 10 Training status feedback phrase logic. #0 #1 #2 #3 #4 #6 NoBelow Aer. low Aer. High Anaerobic #5 Aer. Low result Targets ShortageShortage Shortage Balanced Focus #0 No status 0 1 2 3 4 5 6 #1Detraining 10 11 12 13 14 15 16 #2 Unproductive 70 71 72 73 74 75 76 #3Overreaching 60 61 62 63 64 65 66 #4 Maintaining 30 31 32 33 34 35 36 #5Recovery 20 21 22 23 24 25 26 #6 Peaking 50 51 52 53 54 55 56 #7Productive 40 41 42 43 44 45 46 #9 #10 #11 #7 Above Above Above Aer. #8Targets, targets, targets, #12 High Anaerobic Aer. Low Aer. HighAnaerobic Approaching Focus Focus Focus Focus Focus targets #0 No status7 8 9 80 81 82 #1 Detraining 17 18 19 83 84 85 #2 Unproductive 77 78 79101 102 103 #3 Overreaching 67 68 69 98 99 100 #4 Maintaining 37 38 3989 90 91 #5 Recovery 27 28 29 86 87 88 #6 Peaking 57 58 59 95 96 97 #7Productive 47 48 49 92 93 94

TABLE 11 Example feedback phrases based on relationship between trainingstatus and training distribution. Logic phrase number Example feedbackphrase  0-9, 80-82 “No status: Before we can determine your actualtraining status you typically need a few weeks of training history,including some activities with VO2max results from running or cycling.”11-19, 83-85 “Detraining - You've been training much less thanrecommended for a week or more, and it is starting to affect yourfitness. Try increasing your training load to see improvement.” 22-24“Recovery - Your lighter training load is allowing your body to recover,which is essential during extended periods of hard training. Whenreturning to higher training loads try to achieve better balance betweendifferent intensities.” 25-28 “Recovery - Your lighter training load isallowing your body to recover, which is essential during extendedperiods of hard training. You can return to a higher training load whenyou feel ready.” 21,88 “Recovery - Although your lighter training loadis allowing your body to recover, it is also contributing to yoursuboptimal monthly training load level. In long term, schedule only 1-2easy training weeks for one month to keep monthly load at adequatelevel.” 29, 86, 87 “Recovery - Your recently lowered training load isallowing for recovery, but your longer-term training load is stillhigh.” 32-34 “Maintaining - Your current training is enough to maintainyour fitness level. Keep in mind that significant shortage in anytraining intensities typically causes suboptimal fitness development”.35 “Maintaining - Your current training is enough to maintain yourfitness level. Keep in mind that by alternating easy and hard trainingweeks and training focus periodically might help you to increase yourfitness level” 36-38 “Maintaining - During last month you have beenfocusing on a specific performance element but not seeing immediateresults - that's sometimes the case so stay patient. after focusing longenough for a desired performance element try getting your distributioninto balance” 31, 91 “Maintaining - Your current training is enough tomaintain your fitness level. To see improvement try increasing totalload to target.” 39, 89, 90 “Maintaining - You have been training reallyhard. Remember to include easier training weeks into your schedule toensure adequate recovery and supercompensation.” 42-44 “Productive -Your fitness develops nicely. However, keep in mind that significantshortage in any training intensities causes suboptimal impulse for thosespecific physiological characteristics and increases monotony in termsof physiological training stimuli thus typically resulting in suboptimalfitness development”. 45-48 “Productive - Good job! Your trainingdistribution looks safe and sound and fitness develops nicely” 41, 94“Productive - Your fitness seems to develop nicely despite low monthlytraining load. Try increasing overall load to targets to boost fitnessdevelopment” 49, 92, 93 “Productive - You have been working really hardto get productive! Remember to include casier training weeks to ensureadequate recovery and fitness development in long term” 52-54 “Peaking -You are in ideal race condition! Your recently reduced training load isallowing your body to recover and fully compensate for earlier training.However, keep in mind that significant shortage in any trainingintensities causes suboptimal impulse for those specific physiologicalcharacteristics and increases monotony in terms of physiologicaltraining stimuli thus typically resulting in suboptimal fitnessdevelopment”. 55-58 “Peaking - You are in ideal race condition! Yourrecently reduced training load is allowing your body to recover andfully compensate for earlier training. Be sure to think ahead, sincethis peak state can only be maintained for a short time. 51, 97“Peaking - Your performance seems to develop nicely! Although it seemsthat reduced training load is allowing your body to recover and fullycompensate for earlier training - your past training has already been soeasy that you can't keep up tapering for too long.” 59, 95, 96“Peaking - You are in ideal race condition! Your recently reducedtraining load is allowing your body to recover and fully compensate forearlier training. As your monthly training load is still high you cancontinue recovery period for a while”. 62, 67, 68 “Overreaching - Yourlast week's training has been very hard and passed month's intensitytraining proportion is very high. Training has become counterproductiveand your body needs a rest. When resuming your normal training focussome time on aerobic low intensity training 63-66 “Overreaching - Yourweekly training load is very high and has become counterproductive. Yourbody needs a rest. Give yourself time to recover by adding lightertraining to your schedule. 61, 100 “Overreaching - Your last week'straining has been very hard. Training has become counterproductive andyour body needs a rest. You can try approaching monthly targets a bitmore slowly as this kind of overreaching periods should be done onlyoccasionally. 69, 98, 99 “Overreaching - Both your weekly training loadand monthly training load are very high and training has becomecounterproductive. Your body needs a rest. Give yourself time to recoverby adding lighter training to your schedule. 72 “Unproductive - Highrelative amount of high intensity training may have turned your trainingstatus into Unproductive. Increase aerobic low intensity training to getProductive again” 75-78 “Unproductive - Your training seems OK but yourfitness is decreasing. Your body may be struggling to recover, so besure to pay attention to your overall health including stress, nutritionand rest.” 71 “Unproductive - You should add your long term load. Yourcurrent training load does not seem to be sufficient to achieve fitnessbenefits” 79, 101, 102 “Unproductive - Your body may struggling torecover at the moment due to high monthly training load. You could tryhaving an easier training period for a while.” 73 “Unproductive -Insufficient amount of aerobic high intensity training may have caused atemporary reduction in performance. Try adding high intensity trainingto improve your Fitness level.” 74 “Unproductive - Despite lack ofanaerobic interval workouts your training seems otherwise OK. Your bodymay be struggling to recover, so be sure to pay attention to youroverall health including stress, nutrition and rest.”

In one exemplary embodiment, feedbacks 10, 20, 30, 40, 50, 60 and 70 maynever appear and thus do not receive a feedback phrase. These feedbacksdo not appear since no other training load distribution feedbacks than#1-#12 are allowed when training status number is #14-7.

As can be seen, the above matrix enables very detailed feedback.Alternatively, and as has been shown in

some occasions the in above example table—feedback phrases in certainconditions can be combined which may be done to fit sentences to thecomprehension level of target user level.

FIG. 23 displays an example user watch-style device user interfaceshowing workout result label feedback. Workout labeling helpsgoal-oriented athletes to improve their performance by revealing themain impact brought by the workout:

-   -   Each workout has different primary effect to fitness and        physiological characteristics.    -   Athletes know diverse training is necessary but hard to        implement in practice due to lack of time and experience.    -   Feature summarizes the main benefit/effect gained from exercise.    -   Categorizes the quality of the workout to main three areas of        fitness, used in coaching (base/capacity/power)    -   Based on EPOC, TRIMP and recognized areas of physiological        impact

FIG. 24 displays an example watch-style device user interface showingmultiple screens of training load balance feedback. It shows trainingload balance as calculated training history analysis (THA) libraryoutputs:

-   -   1. Sum of Training Load for past 4 w for each category (Aer.        low, Aer. high, Anaerobic)    -   2. Upper and lower training load limits for target 4 w Training        Load for each category    -   3. Feedback phrase number for Training Load Balance        interpretation    -   4. % distribution to calculate based on Training Load sum values        ETE real-time library will be updated to output Workout Label to        be inputted into THA

The foregoing description and accompanying figures illustrate theprinciples, preferred embodiments and modes of operation of theinvention. However, the invention should not be construed as beinglimited to the particular embodiments discussed above. Additionalvariations of the embodiments discussed above will be appreciated bythose skilled in the art.

Therefore, the above-described embodiments should be regarded asillustrative rather than restrictive. Accordingly, it should beappreciated that variations to those embodiments can be made by thoseskilled in the art without departing from the scope of the invention asdefined by the following claims.

1. A wearable electronic device configured to be worn by a user, thedevice comprising: a display, a memory including historical userinformation; a heart rate sensor configured to generate aphotoplethysmography signal; and a processor coupled with the display,memory, and the heart rate sensor, the processor configured to-calculate exercise intensity information using heart rate informationassociated with the photoplethysmography signal, determine each of anaerobic training load, and aerobic training effect, an anaerobictraining load, and an anaerobic training effect using the exerciseintensity information and the historical user information, identifyintensity categories of aerobic low, aerobic high, and anaerobic basedon the determined training loads and effects, wherein the categoryaerobic high involves a higher heart rate than the category aerobic lowand does not belong in the category of anaerobic, and control thedisplay to present an indication of the identified intensity categories.2. The device of claim 1, wherein the electronic device is selected fromthe group consisting of a smartwatch, a wrist-top computer, and a mobilephone.
 3. The device of claim 1, wherein the electronic device furtherincludes a GPS receiver configured to generate location information andthe processor is coupled with the GPS receiver and configured to-calculate performance information using the location information, andcalculate the exercise intensity information using the heart rateinformation and the performance information.
 4. The device of claim 1,wherein the historical user information includes fitness level data andheart rate data for the user.
 5. The device of claim 1, wherein theprocessor is configured to update the historical user information storedin the memory based on the aerobic training load, the aerobic trainingeffect, the anaerobic training load, and the anaerobic training effect.6. The device of claim 1, wherein the processor is configured to updatethe historical user information stored in the memory based on theidentified intensity categories.
 7. The device of claim 1, wherein theprocessor is further configured to determine at least one of Recovery,Base, Tempo, Lactate Threshold, VO2 max, Anaerobic capacity and speedmetrics using at least one of the aerobic training load, the aerobictraining effect, the anaerobic training load, and the anaerobic trainingeffect.
 8. The device of claim 7, wherein the processor is configured tocontrol the display to present a visual indication of at least one ofthe determined metrics.
 9. The device of claim 1, wherein the processoris configured to control the display to present a trainingrecommendation based on the identified intensity categories.